Fast Processing Time Research Articles

Mixed Integer Linear Programming Applied to Adaptive Directional Overcurrent Protection Considering N-1 Contingency

This work proposes a methodology for directional overcurrent protection coordination in interconnected transmission systems considering a possible network contingency state. The methodology uses the short-circuit data of the current network topology; however, the maximum load current data in the protection section of each relay is obtained considering the n-1 criterion, already foreseeing the disconnection of some network line. Thus, if a line gets disconnected, other protective devices will not improperly actuate by the redistribution of load currents in the network. The objective is to propose an adaptive protection scheme to redo the coordination for each topological change in the network. To this end, this work considers a smart grid environment with a supervisory system with communication capability between this and the remote devices. To obtain the optimal performance, the coordination problem, originally non-linear and non-convex, is linearized, allowing its formulation as a Mixed Integer Linear Programming problem. The methodology is applied to the 8-bus test system in 3 different cases and the 30-bus test system. Results show that the optimal coordination is obtained in a fast computational processing time, showing the suitability of the methodology for real-time application.

Detection of GNSS antenna oscillatory motion and multipath conditions via exploitation of multipath-induced SNR variations

The multipath effect is well known as one of the dominant error sources in most high-precision GNSS applications, as its site-dependent and fast-changing nature render it challenging to model and mitigate using differencing techniques. However, in this study, we present a novel approach of using the multipath effect of GNSS signal in the case of oscillatory motion of a GNSS antenna to determine the characteristics of the oscillatory motion by utilizing the signal-to-noise (SNR) ratio measurements of the GNSS signals and without applying the positioning solution or the use of other sensors. The proposed method is based on the hypothesis that for a short time period, while the multipath conditions remain constant and the satellites are approximately at the same position, an oscillation-type motion of the antenna should generate changes in multipath, expressed as oscillatory pattern variations in the SNR measurements of the satellites. This approach can be used to identify the multipath parameters and extract characteristics of the oscillatory motion of the GNSS antenna. The SNR-based modeling of cm-level antenna oscillations was demonstrated in simulated and field experiments. The benefits of this method include immunity to poor satellite constellation geometry, adaptability to changing multipath geometry and fast processing time. This approach could potentially, under conditions, contribute to GNSS reflectometry (GNSS-R) applications, where the oscillatory motion of the GNSS antenna generates the same effect as the satellite motion-induced multipath, limiting simultaneously the observation periods and the amount of data to be processed.

Face Recognition Technique using YOLO V5 Algorithm

In today’s date real world application like human-machine interaction, security surveillance face recognition has made its great importance. For face recognition the steps to be followed are data collection, preprocessing, Feature Extraction, Training Evaluation and finally testing. One of the best Algorithms used for face recognition is Viola-Jones Algorithm. Viola Jones Algorithm is highly accepted because of its fast processing time and high detection rate. The other detection Algorithms which can be used are HOG Algorithm (Histogram Oriented Gradient), Deep Learning CNN (Convolution Neural Network) Algorithm, Haar cascade Algorithm and MTCNN Algorithm. In this Paper we will discuss about all this Algorithms and compare the detection rate of facial points among various methods for the datasets. YOLO V5 algorithm is another algorithm which can be used for face recognition technique. Face recognition can be achieved by combining the YOLO V5 algorithm with additional technique such as deep face recognition models like face net, which can recognize and compare facial features to identify individuals.YOLOV5 can identify facial features such as eyes, mouth, and nose, making it useful for a variety of face recognition applications.

Optimization of cavitation-assisted biodiesel production and fuel properties from Neochloris oleoabundans microalgae oil using genetic algorithm and response surface methodology

Biodiesel is a promising alternative fuel for diesel engines that provides energy security and significant environmental benefits. But its commercialization is restricted due to the cost and quality as compared to diesel fuel. To address the above, Neochloris oleoabundans microalgae oil, a non-edible, cost-effective, and underutilized third-generation feedstock, is used to produce superior biodiesel by the transesterification reaction using a hydrodynamic cavitation (HC) approach. In this research work, the transesterification model for microalgae biodiesel production is optimized through genetic algorithm (GA) and response surface methodology (RSM) using MATLAB R2020a and Design Expert 8.0.7.1, respectively. Four process input variables (operating pressure (0.5–2.5 bar), catalyst concentration (0.5–1.5 wt.%), reaction time (5–25 minutes), and molar ratio (3:1 to 7:1)) and a five-level L30 array were developed to construct a statistical model. Biodiesel yield and major properties like density, kinematic viscosity, calorific value, and flash point are the model responses. The optimum conditions for microalgae biodiesel production were operating pressure of 2.43 bar, KOH catalyst concentrations of 1.22 wt. %, reaction time of 15.54 minutes, molar ratio of 5.61:1 and at 60°C fixed reaction temperature. The optimum value of response, such as biodiesel yield, was 99.80%, the density was 0.882 g/cc, the kinematic viscosity was 4.06 cst, the calorific value was 41.72 MJ/kg, and the flash point was 146.64 °C. RSM and GA predicted results were validated experimentally. The reaction time for biodiesel produced by HC in the same settings as the conventional method was reduced by 85%. Due to its faster processing time, the HC approach is preferred to the conventional method.

A novel transfer learning model on complex fuzzy inference system

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer’s disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system’s increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model.

Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example

Rapid and accurate monitoring of algal blooms using remote sensing techniques is an effective means for the prevention and control of algal blooms. Traditional methods often have difficulty achieving the balance between interpretative accuracy and efficiency. The advantages of a deep learning method bring new possibilities to the rapid and precise identification of algal blooms using images. In this paper, taking Chaohu Lake as the study area, a dual U-Net model (including a U-Net network for spring and winter and a U-Net network for summer and autumn) is proposed for the identification of algal blooms using remote sensing images according to the different traits of the algae in different seasons. First, the spectral reflection characteristics of the algae in Chaohu Lake in different seasons are analyzed, and sufficient samples are selected for the training of the proposed model. Then, by adding an attention gate architecture to the classical U-Net framework, which can enhance the capability of the network on feature extraction, the dual U-Net model is constructed and trained for the identification of algal blooms in different seasons. Finally, the identification results are obtained by inputting remote sensing data into the model. The experimental results show that the interpretation accuracy of the proposed deep learning model is higher than 90% in most cases with the fastest processing time being less than 10 s, which achieves much better performance than the traditional supervised classification method and also outperforms the single U-Net model using data of whole year as the training samples. Furthermore, the profiles of algal blooms are well-captured.

Automatic identification system-based trajectory clustering framework to identify vessel movement pattern

<span lang="EN-US">Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.</span>

Revolutionizing Data Entry: An In-Depth Study of Optical Character Recognition Technology and Its Future Potential

Abstract: This research paper delves into the advancements and potential of Optical Character Recognition (OCR) technology for revolutionizing data entry processes. The study provides a comprehensive analysis of the history, current state, and future of OCR technology. The paper begins with an overview of the evolution of OCR technology and its various applications in document scanning, indexing, and management. The research then examines the benefits of OCR technology, such as improved accuracy, faster processing times, and cost savings compared to traditional data entry methods. It also addresses the challenges faced by OCR technology, including difficulties in recognizing handwritten text and the need for continuous improvement and innovation. The paper concludes with a discussion of the future potential of OCR technology, including the integration of artificial intelligence and machine learning for improved accuracy and efficiency, and the potential for increased automation in various industries. The study highlights the importance of OCR technology for revolutionizing data entry processes and its potential for further development and growth in the future

Automatic Short Answer Grading onHigh School’s E-Learning Using Semantic Similarity Methods

Grading students’ answers has always been a daunting task which takes a lot of teachers’ time. The aim of this study is to grade students’ answers automatically in a high school’s e-learning system. The grading process must be fast, and the result must be as close as possible to the teacher assigned grades. We collected a total of 840 answers from 40 students for this study, each already graded by their teachers. We used Python library sentence-transformers and three of its latest pre-trained machine learning models (all-mpnet-base-v2, all-distilroberta-v1, all-MiniLM-L6-v2) for sentence embeddings. Computer grades were calculated using Cosine Similarity. These grades were then compared with teacher assigned grades using both Mean Absolute Error and Root Mean Square Error. Our results showed that all-MiniLM-L6-v2 gave the most similar grades to teacher assigned grades and had the fastest processing time. Further study may include testing these models on more answers from more students, also fine tune these models using more school materials.

Efficient Convolutional Neural Networks for Semiconductor Wafer Bin Map Classification.

The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment in which the defect occurred, but automating pattern classification is difficult to apply to actual manufacturing sites unless processing speed and resource efficiency are supported. The purpose of this study was to classify these defect patterns with a small amount of resources and time. To this end, we explored an efficient convolutional neural network model that can incorporate three properties: (1) state-of-the-art performances, (2) less resource usage, and (3) faster processing time. In this study, we dealt with classifying nine types of frequently found defect patterns: center, donut, edge-location, edge-ring, location, random, scratch, near-full type, and None type using open dataset WM-811K. We compared classification performance, resource usage, and processing time using EfficientNetV2, ShuffleNetV2, MobileNetV2 and MobileNetV3, which are the smallest and latest light-weight convolutional neural network models. As a result, the MobileNetV3-based wafer map pattern classifier uses 7.5 times fewer parameters than ResNet, and the training speed is 7.2 times and the inference speed is 4.9 times faster, while the accuracy is 98% and the F1 score is 89.5%, achieving the same level. Therefore, it can be proved that it can be used as a wafer map classification model without high-performance hardware in an actual manufacturing system.

A Systematic Review: Comparison of Immunocytochemistry, ELISA, and Western Blot Methods in Alkaline phosphatase Measurement at Genistein-induced Osteoblast Cell

Osteoporosis is a bone disorder characterized by the decrease of bone mass along with bone micro-architecture damage and has a risk become a fracture. One of the causes of osteoporosis is estrogen deficiency. Genistein is a phytoestrogen compound in the isoflavone group containing a similar structure compared to 17β-estradiol, thus it can bind to estrogen receptors and produce an estrogenic effect. Genistein induction can stimulate bone formation and promote the increase of alkaline phosphate (ALP) activities in osteoblast cells which can be observed by immunocytochemistry or Enzyme-linked Immunosorbent Assay (ELISA) or Western blot method. Using the PRISMA guideline technique, choose and strategize article searches by reading the title, abstract, and then the whole text of the article. Articles with the keywords "genistein or osteoblast cells or alkaline phosphate or immunocytochemistry or immunofluorescence or ELISA or western blot" were retrieved from databases including Google Scholar, PubMed, Researchgate, and Sciencedirect. 24 relevant research articles were uncovered as a result of this systematic review. Comparison of immunocytochemistry and ELISA methods in order to analyze the activities of ALP in osteoblast induced by genistein includes selectivity, sensitivity, processing time, and cost efficiency parameters. The immunocytochemistry method has a higher level of sensitivity and a faster processing time, whereas the ELISA method has a higher level of selectivity and less cost efficiency. The western blot method has selectivity for detecting complex-level protein expression.

NIMG-108. PHYSICAL STRESSES IN BRAIN TUMORS

Abstract Solid stress, distinct from fluid pressure, is a physical force contained in and transmitted by solid components of the brain tumor, including cells and the matrix they produce. Solid stress has been shown to promote tumor progression, and decrease anticancer therapy efficacy. This is especially relevant in brain tumors, as the rigid skull results in these trapped forces, increasing intracranial pressure, and potentially leading to other complications, including neuronal cell death. Here we present a novel method of quantifying these physical stresses in situ in both mice (glioblastoma [U87], brain metastasis [BT474], and ependymoma models) and patients. Briefly, following a craniotomy, mechanical forces that include solid stress are released, which causes the tissue to deform in peaks (areas under compression) and valleys (areas originally under tension). This tissue deformation is imaged via high-resolution ultrasound and analysed via custom MATLAB code to produce an accurate 3D model of the entire mouse brain, including the tumour region. For human samples, a pre-operative MRI is used to generate a detailed 3D model of the human brain. During surgery, the trapped physical stresses results in a bulge of the dura post craniotomy. We use BrainLab to measure the craniotomy induced brain deformation, which is then registered to the pre-operation MRI before being analysed in an identical fashion to the murine models using SolidWorks and Abaqus. We further show that in the brain metastases model, chemotherapy reduces compression stresses by 51%. Further, our technique results in fast processing time (~ 15 minutes), and has the potential to prevent the need for intraoperative MRI based on position simulations. As such, solid stress measurements provide a new class of mechanical biomarkers that can be correlated to clinical outcomes for predictive and prognostic value.

Optimization of Simple Additive Weighting Method in Assessment of Research Reviewer Selection

Quality research will not be separated from controlling systems that require a review mechanism. This demand considers it necessary to form an assessment committee or reviewer that ensures that all processes proceed towards the target target. The internal reviewer selection process is carried out by looking at several requirements of each prospective reviewer. The selection process is carried out by looking at the requirements files one by one. For this reason, it is necessary to optimize the method that is able to manage the assessment data of prospective reviewers who have the highest rating value from the results of weight calculations. Decision making in determining internal reviewers requires a method that can provide optimal decision results in terms of relatively fast processing time. The decision support method applied in determining internal reviewers is Simple Additive Weighting (SAW). The reason for choosing the SAW method in this study, the method has a basic concept that is used to find weight values on the performance rating of each alternative on all attributes. The SAW method is commonly known as the weighted summation method. There are six criteria used and fifty-five records for alternatives used. The results of the SAW method ranking obtained by A20 have the highest preference value of 0.77. This study shows the optimality of the SAW method in providing decision results based on an accuracy test value of 80%.

Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source.

Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution, and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 90.3%, 56.0%, and 187.4% compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. With a fast processing time of SVD (∼50 µs per frame), the proposed method is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.

Diagnosis of pathogen infection via a multiple-wavelength colorimetric sensor platform with loop-mediated isothermal amplification

Diagnosis of pathogen infection based on loop-mediated isothermal amplification (LAMP) is suitable for the point-of-care test due to fast process time, cost-effectiveness, and high specificity for target deoxyribonucleic acids (DNAs). In this diagnosis method, the presence of a target DNA in a tested solution can be determined by the color change during the LAMP reaction and the color change is typically monitored by naked-eye observation. However, although the evaluation by naked-eye is simple and fast, human subjectivity can significantly affect the diagnosis result because of an inconsistent positive threshold for diagnosis. In this study, we developed a colorimetric sensor platform that can accurately monitor the color change of LAMP solutions in 8-tube strips for diagnosing pathogen infection. To determine color change from a shift of light spectrum, this sensor contains eight pairs of red/green/blue (RGB) light-emitting diodes and RGB photodetectors to monitor a color change in a broad visible wavelength region. We also developed an evaluation algorithm based on a correlated-variation nulling technique to maximize the distance between negative and positive output distribution bands and calibrate the inherent tube-to-tube variation of color changes. The accuracy of the developed method was evaluated by testing 233 clinical samples for diagnosing Streptococcus agalactiae (also known as group B Streptococcus) bacterial infection and comparing the results with that by agarose gel electrophoresis. The accuracy was as high as 99.1%. This method can be one of standard diagnostic assays for gene amplification-based diagnosis of pathogen infection.

ROBOTIC PROCESS AUTOMATION FOR QUALITY CONTROL ASSESSMENT USING SELENIUM

Robotic Process Automation (RPA) is a form of technology to automate tasks and routines performed by humans, which can be instructed to machine. Along with the rapid development of technology, the time that can be used to maintain the quality of a technology is becoming increasingly narrow. Therefore, RPA will imitate activities or tasks performed by humans on a computer with a much faster processing time and 100% accuracy without "human error". The agile method was chosen in this study because of its focus on flexibility and more responsiveness to required changes. The system developed in this paper uses the Selenium library with the Python programming language and retrieves task data from the MNC IT team, especially the QC division for the RCTI+ website Quality Assessment process. From the results of testing this system, it shows that the testing process using this system and testing from a tester / examiner get the same results. However, because this system is programmed, it will have more stable and efficient results.

Deep Learning-Based Automated Cardiac Sub-Structure Contouring with Dosimetric and Clinical Outcomes Validation

<h3>Purpose/Objective(s)</h3> Coronary artery (CA) radiation dose is an independent predictor of major adverse cardiac events (MACE) and all-cause mortality (ACM) following thoracic radiotherapy (RT) for locally-advanced non-small cell lung cancer (LA-NSCLC). However, cardiac sub-structure segmentation is not routinely performed, in part due to technical complexity. Here, we developed a deep learning-based automated cardiac sub-structure contouring tool and compared the overlap with manual contours as well as associations with dosimetric, cardiac, and survival outcomes. <h3>Materials/Methods</h3> A U-Net with ResNet encoders and cross connections was trained to localize the heart and segment its substructures, which included the four chambers and CA (left main, left anterior descending [LAD], left circumflex, right and posterior descending). Our cohort of 700 LA-NCSLC patients treated with RT with non-gated planning CT scans (with and without contrast) and manually segmented cardiac sub-structures was split into training (n=560), validation (n=70), and test (n=70) groups. Validation was performed by comparing the prediction against the manual contours. Geometric analysis evaluated Dice coefficient, overlap measures, and average symmetrical surface distance (ASSD). Dosimetric validation evaluated mean, maximum, and volume (V) receiving 15 Gy (V15Gy). The discrete Fréchet distance evaluated similarity of dose-volume histograms. Cox and Fine and Gray regressions (non-cardiac death as a competing risk) were used for clinical validation. <h3>Results</h3> The average processing and segmentation time was 51.5±9.6 seconds and 2.96±1.51 seconds, respectively. The median dice for the heart was 0.94 [IQR=0.02] and 0.84 [IQR=0.06] for the chambers. The median overlap for the CA was 0.87 [IQR=0.27] with an ASSD of (2.03±1.31) mm. The mean difference between the prediction and ground truth for the mean dose was (0.09±0.64 Gy for the heart, (-0.44±2.16 Gy for the chambers, and -0.35±3.20) Gy for the CA. The maximum dose differed by (-0.9±7.3 Gy. The difference of the V15Gy for the LAD was 0.17+7.34 %. The correlation between manual and prediction based on a linear regression was R²=0.88. The median similarity between DVHs for chambers and CA was 0.99 [IQR=0.31] and 0.80 [IQR=0.46]. In the test group, adjusting for pre-existing coronary heart disease, predicted LAD V15Gy was associated with an increased risk of MACE (HR 1.02 per %, 95% CI 1.00-1.04; p=0.012) and ACM (HR 1.02 per %, 95% CI 1.00-1.03; p=.046). <h3>Conclusion</h3> We developed deep learning automated cardiac sub-structure segmentation algorithms from a large real-world planning CT dataset. The algorithms produced high quality contours with high geometric overlap with manual contours, and were associated with dosimetric, cardiac, and survival outcomes. High segmentation quality and fast processing time will enable clinical translation to improve cardiac toxicity risk prediction.

Ultrafast PCR Detection of COVID-19 by Using a Microfluidic Chip-Based System.

With the evolution of the pandemic caused by the Coronavirus disease of 2019 (COVID-19), reverse transcriptase-polymerase chain reactions (RT-PCR) have invariably been a golden standard in clinical diagnosis. Nevertheless, the traditional polymerase chain reaction (PCR) is not feasible for field application due to its drawbacks, such as time-consuming and laboratory-based dependence. To overcome these challenges, a microchip-based ultrafast PCR system called SWM-02 was proposed to make PCR assay in a rapid, portable, and low-cost strategy. This novel platform can perform 6-sample detection per run using multiple fluorescent channels and complete an ultrafast COVID-19 RT-PCR test within 40 min. Here, we evaluated the performance of the microdevice using the gradient-diluted COVID-19 reference samples and commercial PCR kit and determined its limit-of-detection (LoD) as 500 copies/mL, whose variation coefficients for the nucleocapsid (N) gene and open reading frame 1 ab region (ORF1ab) gene are 1.427% and 0.7872%, respectively. The system also revealed an excellent linear correlation between cycle threshold (Ct) values and dilution factors (R2 > 0.99). Additionally, we successfully detected the target RNAs and internal gene in the clinical samples by fast PCR, which shows strong consistency with conventional PCR protocol. Hence, with compact dimension, user-friendly design, and fast processing time, SWM-02 has the capability of offering timely and sensitive on-site molecular diagnosis for prevention and control of pathogen transmission.

Optical Soldering of MoS2 Layers for Defect Structure Formation with Induced Photoluminescence

AbstractLocal manipulation of the defect structures in 2D semiconducting transition metal dichalcogenide (2D TMD) opens rich research avenues harnessing their unique optoelectronic properties, where reliable defect structure formation methods are highly desirable. Here, the concept of optical soldering (OS) on few‐layer MoS2 to form site‐selective defect structures with induced photoluminescence (PL) is proposed and demonstrated. OS is initiated by optical heating of the top MoS2 layer to elevate the local temperature above the melting point of the underlying indium‐tin‐oxide nanoparticles (ITO‐NPs). In turn, thermally deformed ITO‐NPs pull the top MoS2 layer down to the substrate, forming the OS‐induced defect structure on the optically irradiated region. The OS‐induced defect structures on a few‐layer MoS2 show bright PL, which can be manipulated in peak positions by varying the incident optical power and irradiation time. OS is a facile method for local defect structure patterning on few‐layer MoS2 and manipulation of their optoelectronic properties while requiring low incidence optical power (≈2.5 mW) and fast process time (≈1 s), which are beneficial in the development of 2D TMD material‐based applications.

Applying machine learning algorithms for PE-header-based malware detection on the Windows operating system

Introduction: The rapid growth of malware and its malicious use result in significant financial losses for various organizations. Many researchers are interested in applying machine learning methods to solve the problem of malware detection. Nevertheless, because of the diversity of algorithms, each machine learning algorithm has its advantages and disadvantages for a given situation. Purpose: To apply machine learning for malware detection in the Windows operating system using Portable Executable header; to compare six different machine learning algorithms based on several criteria. Results: The comparison of various algorithms, including such classifiers as Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, Multilayer Perceptron, k-Nearest Neighbors algorithm with a large dataset shows that some algorithms such as Random Forest, Decision Tree, k-Nearest Neighbors, and Multilayer Perceptron can detect malware with very high accuracy (&gt; 98%). The Random Forest algorithm is especially well suited for Windows OS malwaredetection. At the same time, Naive Bayes classifier also has a high accuracy rate (&gt; 96%) and fast processing time. Therefore, we may consider using Naive Bayes as an alternative.