Standard Computer Research Articles

Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment

In this study, a new method was developed to detect student involvement in the online learning process. This method is based on convolutional neural network (CNN) as a classifier with an emphasis on the preprocessing process combined with a new feature in the form of signal magnitude area (SMA) thresholding. In this study, the data used as training data is a public dataset that emphasizes the decomposition of electroencephalography (EEG) signals into individual signal processing. Twenty subjects were taken to be used as test data, with each subject watching online learning lectures in the field of computer science on three different devices, either with a flat screen, a curved screen or a smartphone screen that is smaller than two standard computer monitors. Based on the study's results, it is known that the change in screen size is inversely proportional to the level of student attention, the smaller the screen, the lower the student's attention. For classification results, the model equipped with SMA thresholding outperformed the standard classifier by 8.33% with a test set of 20 people.

CAPTAIN: Community-based Advanced Persistent Threat Analysis in IT Networks

Organizations that possess valuable information assets and critical infrastructure are prone to Advanced Persistent Threats (APTs). The life cycle of this type of modern attack consists of multiple stages called Intrusion Kill Chain (IKC). As one of the most common approaches to deal with these attacks, organizations’ security staff use various heterogeneous security and non-security sensors in different lines of defense (Network, Host, and Application) as the primary detection levels in the monitored IT network to log the attacker’s intrusive activities. They then model their behaviors by using logged events to detect the IKC of APT attacks. However, numerous methods proposed in the literature have three primary drawbacks: 1) the inability to use both security and non-security sensors of the three mentioned detection levels in event correlation analysis, 2) high dependence on expert knowledge in setting up and maintaining common attack patterns, and 3) incapability to provide a visual representation of the attack path for security administrators to better track on-the-fly attacks in a monitored network. This paper presents a system for Community-based Advanced Persistent Threat Analysis in IT Networks (CAPTAIN) to address the aforementioned issues and challenges. The CAPTAIN framework comprises two distinct phases (including 12 different activities) that receive raw events logged by heterogeneous sensors as input and detect possible IKCs of the APT attacks as output. This system implements a novel graph-based attackers’ behavior modeling technique for detecting the IKC of APT attacks by correlating analysis of logged events and leveraging knowledge discovery on the graph. Our evaluation of the two publicly available standard datasets, Bryant and DARPA Transparent Computing, indicates that the CAPTAIN is robust, reliable against high volume events, and can detect the IKC of APT attacks with high accuracy and low false positive rates.

Assessing the Causes of Students' Anxiety on Measurement and Evaluation in Institution of Higher Learning

This study aimed to assess the causes of students' anxiety related to measurement and evaluation in institutions of higher learning in Anambra State, Nigeria. The research design employed was a descriptive survey, utilizing a cross-sectional survey questionnaire to collect quantitative data from a sample of 122 students. The questionnaire consisted of two clusters and ten items each, employing a 4-point Likert scale for responses. The research design employed face validation by experts and achieved acceptable reliability through Cronbach's coefficient alpha. Data analysis involved descriptive statistics, including mean and standard deviation computations using SPSS version 22. The findings revealed that navigating evolving friendships and relationships, learning disabilities, adjusting to a new environment, separation from the support system of family and friends, irregular and poor-quality sleep, curriculum difficulties, discrimination, poor performance history, and a perceived lack of preparation were identified as significant causes of anxiety among students. The study's results provide insights into the specific factors contributing to student anxiety in the context of measurement and evaluation in higher learning institutions. These findings highlight the need for institutions to develop targeted interventions and support systems to address these causes of anxiety and promote students' mental well-being.

A New Algorithm for Large-Scale Geographically Weighted Regression with K-Nearest Neighbors

Geographically weighted regression (GWR) is a classical method for estimating nonstationary relationships. Notwithstanding the great potential of the model for processing geographic data, its large-scale application still faces the challenge of high computational costs. To solve this problem, we proposed a computationally efficient GWR method, called K-Nearest Neighbors Geographically weighted regression (KNN-GWR). First, it utilizes a k-dimensional tree (KD tree) strategy to improve the speed of finding observations around the regression points, and, to optimize the memory complexity, the submatrices of neighbors are extracted from the matrix of the sample dataset. Next, the optimal bandwidth is found by referring to the spatial clustering relationship explained by K-means. Finally, the performance and accuracy of the proposed KNN-GWR method was evaluated using a simulated dataset and a Chinese house price dataset. The results demonstrated that the KNN-GWR method achieved computational efficiency thousands of times faster than existing GWR algorithms, while ensuring accuracy and significantly improving memory optimization. To the best of our knowledge, this method was able to run hundreds of thousands or millions of data on a standard computer, which can inform improvement in the efficiency of local regression models.

Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts

In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results confirm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes.

The Use of Digital Image Processing Method to Estimate the Foam Characteristics of Polyurethane

AbstractFour air‐bubbled polyurethane (PU) foams with different polyol:PMDI wt.% are produced, respectively. The chemical reaction mechanisms of polyurethane and bubble formation are proposed by performing standard Gibbs free energy calculations using the DFT M06‐2X/6‐31+G(d,p) method. The local minima, transition states, and intermediates in reaction mechanisms are detected. It is concluded that both reactions are exothermic. Then, raw images of the produced PU foams are taken with a 13 MP mobile phone camera, which can be considered inexpensive, and the mean radii of the pores are calculated by an image processing based method (IPBM) on a standard desktop computer with an i5 processor. It is determined that there is a close relationship between the calculated mean radius and instrumentally measured thermal conductivity coefficient of the foams. However, the thermal conductivity coefficients are independent of the calculated number and percentage of the pores. The mean radii of the samples calculated by the proposed IPBM are close to that of the SEM, with acceptable relative errors of less than 10%. Finally, it is concluded that IPBM, which is a more cost‐effective, cleaner, and faster method than SEM, might replace SEM in the air bubble analysis of PU foams.

Stereo X-Ray Tomography

X-ray tomography is a powerful volumetric imaging technique, but detailed three dimensional (3D) imaging requires the acquisition of a large number of individual X-ray images, which is time consuming. For applications where spatial information needs to be collected quickly, for example, when studying dynamic processes, standard X-ray tomography is therefore not applicable. Inspired by stereo vision, in this paper, we develop X-ray imaging methods that work with two X-ray projection images. In this setting, without the use of additional strong prior information, we no longer have enough information to fully recover the 3D tomographic images. However, up to a point, we are nevertheless able to extract spatial locations of point and line features. From stereo vision, it is well known that, for a known imaging geometry, once the same point is identified in two images taken from different directions, then the point’s location in 3D space is exactly specified. The challenge is the matching of points between images. As X-ray transmission images are fundamentally different from the surface reflection images used in standard computer vision, we here develop a different feature identification and matching approach. In fact, once point like features are identified, if there are limited points in the image, then they can often be matched exactly. In fact, by utilising a third observation from an appropriate direction, matching becomes unique. Once matched, point locations in 3D space are easily computed using geometric considerations. Linear features, with clear end points, can be located using a similar approach.

Robust design procedure for RC plates and shell structures using developed advanced sandwich model

In recent years, a notable increase in the number of complex concrete roofs has been observed. Simultaneously, the design codes lack design approaches for such structures in which the elements are subjected to combined forces (membrane and transverse) and moments (flexure and torsion). The finite element-based design (FEBD) has been provided for these structural elements; however, it is not widely adopted for the design. Hence, there is an urgent need to fill this gap. Therefore, an automatic FEBD procedure is developed in this study based on the equilibrium consideration, named the developed advanced sandwich model (DASM). The element cracking inspection was performed according to the multi-axial compression state of concrete. Subsequently, the DASM was developed to account for the effect of transverse shear forces. The proposed design algorithm was developed using a Python script linked with ABAQUS software to perform an automatic design for all elements and to be presented as a practical design tool. Additionally, the damage-based analysis was performed as an assessment tool to demonstrate the efficiency of the proposed design procedure. This study illustrated that using the DASM reduced the steel reinforcement by up to 40% and increased the ductility by 10–15%. Also, it is observed that the ultimate capacity of the considered examples, including the solid plate and flat plate, slightly decreased by 4.1% and 1.8% (less than 5%), respectively, which hardly affects the overall response of the designed structure. Indeed, these results demonstrate the efficiency of the developed procedure. In summary, the results of this study indicate that the proposed design framework (DASM) presents a highly accurate, relatively simple, and robust procedure for designing plate and shell structures with complex geometry, where the design process can be completed on a standard personal computer in a few seconds.

Calibration of head mounted displays for vision research with virtual reality.

Immersion in virtual environments is an important analog for scientists. Situations that cannot be safely organized in the real world are being simulated virtually to observe, evaluate, and train aspects of human behavior for psychology, therapy, and assessment. However, creating an immersive environment using traditional graphics practices may create conflict with a researcher's goal of evaluating user response to well-defined visual stimuli. Standard computer monitors may display color-accurate stimuli, but it is generally viewed from a seating position, where the participant can see real-world visual context. In this article, we propose a novel means to allow vision scientists to exert finer control over the participants visual stimuli and context. We propose and verify a device-agnostic approach to color calibration by analyzing display properties such as luminance, spectral distribution, and chromaticity. We evaluated five different head-mounted displays from different manufacturers and showed how our approach produces conforming visual outputs.

Navigation with minimal occupation volume for teleoperated snake-like surgical robots: MOVE.

Master-Slave control is a common mode of operation for surgical robots as it ensures that surgeons are always in control and responsible for the procedure. Most teleoperated surgical systems use low degree-of-freedom (DOF) instruments, thus facilitating direct mapping of manipulator position to the instrument pose and tip location (tip-to-tip mapping). However, with the introduction of continuum and snake-like robots with much higher DOF supported by their inherent redundant architecture for navigating through curved anatomical pathways, there is a need for developing effective kinematic methods that can actuate all the joints in a controlled fashion. This paper introduces the concept of navigation with Minimal Occupation VolumE (MOVE), a teleoperation method that extends the concept of follow-the-leader navigation. It defines the path taken by the head while using all the available space surrounding the robot constrained by individual joint limits. The method was developed for the i 2 Snake robot and validated with detailed simulation and control experiments. The results validate key performance indices such as path following, body weights, path weights, fault tolerance and conservative motion. The MOVE solver can run in real-time on a standard computer at frequencies greater than 1kHz.

Energy behaviour and economic analysis of a photovoltaic-thermal (PV/T) collector coupled with a reverse osmosis (RO) desalination unit in Tunisian climatic conditions: a feasibility study.

Water scarcity affects about onebillion people in the world. Around two billion people could be living in water-stressed areas by 2050. For this reason, the desalination is always evolving due to the importance of the water resources found in the seas and brackish water. As these systems are generally energy intensive, the use of a renewable energy source is among the most appropriate solution. In this paper, both experimental and numerical investigations have been conducted to evaluate the performances and the economic viability of aphotovoltaic-thermal collector intended to supply a reverse osmosis (RO) unit. Experimental study is based on the input-output and dynamic system testing (DST)according to ISO 9459-5 standard method and computations use the energy and mass balances of the PV/T collector and the RO plant. Results of DST testing showed that the loss coefficient of the PV/T, the tank loss coefficient and the total tank heat capacity are 10.46 W.m-2.K-1, 1.596 W.K-1 and 388MJ.K-1, respectively. The ability to couple the RO technology to PV/T systems has been demonstrated. The complete system has been simulated for a water salinity of 10,000ppm and climatic data of Borj-Cedria (Tunisia) site (longitude 10° 25' 41″ E and latitude 36° 43' 04″ N). Numerical investigations showed that the electricity needs of a small off-grid desalination unit could be met by using a 6.48m2 PV/T panel surface area. In this case, the purified water produced has a salinity of 1500ppm and the flow rate is 24,000l/day. For a grid connected site, the produced and auxiliary powers are found to be equal to 54% and 21%, respectively. Moreover, the economic cost of adding a PV/T system into an existing RO unit has been evaluated and the results showed that the payback period is 6years.

Reactive Message Passing for Scalable Bayesian Inference

We introduce reactive message passing (RMP) as a framework for executing schedule-free, scalable, and, potentially, more robust message passing-based inference in a factor graph representation of a probabilistic model. RMP is based on the reactive programming style, which only describes how nodes in a factor graph react to changes in connected nodes. We recognize reactive programming as the suitable programming abstraction for message passing-based methods that improve robustness, scalability, and execution time of the inference procedure and are useful for all future implementations of message passing methods. We also present our own implementation ReactiveMP.jl, which is a Julia package for realizing RMP through minimization of a constrained Bethe free energy. By user-defined specification of local form and factorization constraints on the variational posterior distribution, ReactiveMP.jl executes hybrid message passing algorithms including belief propagation, variational message passing, expectation propagation, and expectation maximization update rules. Experimental results demonstrate the great performance of our RMP implementation compared to other Julia packages for Bayesian inference across a range of probabilistic models. In particular, we show that the RMP framework is capable of performing Bayesian inference for large-scale probabilistic state-space models with hundreds of thousands of random variables on a standard laptop computer.

THE LEVEL OF TROPHOBLAST HORMONES IN EARLY PREMATURE TERMS OF GESTATION

Abstract. The trophoblast hormones of the chorionic villi (CV) of the placenta play a signifi cant role in the processes of formation of structures and functions of the fetus. Disorders of microcirculation also lead to disturbances in metabolism and oxygenation, which causes fetal distress. Microcirculation disorders also aff ect the hormone- producing function of the placental complex. Disorders of fetoplacental blood fl ow at the beginning of the second trimester lead to a drop in the synthesis of hormones by half compared to the norm and the appearance of clinical manifestations of placental dysfunction. There is a pronounced drop in the hormoneproducing function of the placenta in pregnant women with miscarriage and disorders in the mother- placentafetus system at 30-32 weeks, which confi rms the failure of compensation of the placental system.The purpose. The aim of the study was to quantitatively establish the concentration of chorionic gonadotropin and placental lactogen in the trophoblast of the chorionic villi of the placenta in the early stages of pregnancy in pregnant women with miscarriage in order to understand the pathogenesis of placental insu ffi ciency in the fi rst trimester of pregnancy.Research material and methods. The immunohistochemical quantitative investigations of hormones of trophoblast of chorial villi were made at early stages of pregnancy (5-12 weeks). The material of spontaneous miscarriages of 5-12 weeks of gestation was studied (28 observations – the main group). The material of abortions from practically healthy women for social reasons (16 cases) served as a control. Scientifi c studies passed the bioethics commission, no violations were found. Scientifi c research passed the bioethics commission, no violations were found (protocol N 4 dated December 16, 2021). Standard methods of mathematical analysis using Student’s and Fisher’s criteria, standard and specialized computer programs were used for statistical processing of the obtained data. Pairwise correlation indicators were used to study the nature and degree of relationship between various parameters.Results and their discussion. It was established that the signifi cant fl uctuations of the «optical density» indicator in relation to the content of CHT in the trophoblast of HC in the main group are actually caused by two types of pathology, one of which is accompanied by a decrease in the concentration of CHT (13 observations – main group A), and the other – on the contrary, by its increase (15 observations – main group B).It was established that in principle may developed two types of placental (villous) insu ffi ciency, and the common sings of its are decreased of vascularization of chorial villi. The fi rst type is described as decreased concentration of chorionic gonadotropin and placental lactogen in the trophoblast, morphological sings of slowdown of development of chorial villi with increasing formation of cytotrophoblast of chorial villi. The second type show itself increasing of concentration of this hormones in the trophoblast and decreasing of processes of formation of cytotrophoblast of chorial villi.Conclusions. Both variants of the described pathology in relation to CHT and PLH (lower and higher concentration of them in the trophoblast of HC) in combination with a certain morphological picture, in our opinion, are the molecular and structural basis of the two types of placental (villous) insu ffi ciency in the early stages of gestation, respectively.

Does virtual reality enhance the effects of inhibitory control training for loss-of-control eating? A pilot factorial experiment

Inhibitory control, one's ability to inhibit automatic responses to desirable stimuli, may be inadequately targeted in interventions for loss-of-control eating (LOC). Promising evidence has identified inhibitory control trainings (ICTs) as an avenue to target inhibitory control directly; however, effects of ICTs on real-world behavior are limited. Compared to typical computerized trainings, virtual reality (VR) presents several potential advantages that may address key shortcomings of traditional ICTs, i.e. poor approximation to everyday life. The present study utilized a 2 × 2 factorial design of treatment type (ICT vs sham) by treatment modality (VR vs standard computer), which allows for increased statistical power by collapsing across conditions. Our primary aim was to examine the feasibility and acceptability of six weeks of daily training among groups. A secondary aim was to preliminarily assess main and interactive effects of treatment type and modality on target engagement and efficacy (i.e., training compliance, change in LOC episodes, inhibitory control, and implicit liking of foods). Participants (N = 35) with ≥1×/weekly LOC were assigned to one of four conditions and completed ICTs daily for six weeks. The trainings were feasible and acceptable, evinced by high retention and compliance across time and condition. Although completing daily trainings across treatment types and modalities was associated with large decreases in LOC, there were no meaningful effects of either treatment type or modality, nor a significant interaction effect, on LOC or mechanistic variables. Future research should aim to increase the efficacy of ICT (both standard and VR-based) and test in fully-powered clinical trials.

Fast Substructure Search in Combinatorial Library Spaces.

We present an efficient algorithm for substructure search in combinatorial libraries defined by synthons, i.e., substructures with connection points. Our method improves on existing approaches by introducing powerful heuristics and fast fingerprint screening to quickly eliminate branches of nonmatching combinations of synthons. With this, we achieve typical response times of a few seconds on a standard desktop computer for searches in large combinatorial libraries like the Enamine REAL Space. We published the Java source as part of the OpenChemLib under the BSD license, and we implemented tools to enable substructure search in custom combinatorial libraries.

STUDY OF PERMEABILITY PREDICTION USING HYDRAULIC FLOW UNIT (HFU) AND MACHINE LEARNING METHOD IN “BSH” FIELD

In this study, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, Support Vector Machines, and K-Nearest Neighbor Machine Learning model are presented that use log and core data available as the basis for permeability prediction. The results were then compared to previously available method, namely Hydraulic Flow Unit (HFU) based on MAE and RMSE Value. The approach was taken by considering correlation relationships between existing log data in predicting permeability values. Three correlations, namely Spearman, Pearson, and Kendall, will be used to determine the relationship between existing log data and permeability. The machine learning model is then compared with the Hydraulic Flow Unit (HFU) Method in predicting the permeability value. The Novelty of this Machine Learning Model is to be able to predict permeability value, to solve the problem of accuracy using the existing method, and to save reasonable time to obtain permeability value by coring in the laboratory by utilizing standard computer available.

A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields

Modeling typhoon-induced storm surges requires 10-m wind and sea level pressure fields as forcings, commonly obtained using parametric models or a fully dynamical simulation by numerical weather prediction (NWP) models. The parametric models are generally less accurate than the full-physics models of the NWP, but they are often preferred owing to their computational efficiency facilitating rapid uncertainty quantification. Here, we propose using a deep learning method based on generative adversarial networks (GAN) to translate the parametric model outputs into a more realistic atmospheric forcings structure resembling the NWP model results. Additionally, we introduce lead-lag parameters to incorporate a forecasting feature in our model. Thirty-four historical typhoon events from 1981 to 2012 are selected to train the GAN, followed by storm surge simulations for the four most recent events. The proposed method efficiently transforms the parametric model into realistic forcing fields by a standard desktop computer within a few seconds. The results show that the storm surge model accuracy with forcings generated by GAN is comparable to that of the NWP model and outperforms the parametric model. Our novel GAN model offers an alternative for rapid storm forecasting and can potentially combine varied data, such as those from satellite images, to improve the forecasts further.

A comparison of perceived image quality between computer display monitors and augmented reality smart glasses

IntroductionAugmented-reality (AR) smart glasses provide an alternative to standard computer display monitors (CDM). AR smart glasses may provide an opportunity to improve visualisation during fluoroscopy and interventional radiology (IR) procedures when there can be difficulty in viewing intra-procedural images on a CDM. The aim of this study was to evaluate radiographer perception of image quality (IQ) when comparing CDM and AR smart glasses. Methods38 radiographers attending an international congress evaluated ten fluoroscopic-guided surgery and IR images on both a CDM (1920 × 1200 pixels) and a set of Epson Moverio BT-40 AR smart glasses (1920 × 1080 pixels). Participants provided oral responses to pre-defined IQ questions generated by study researchers. Summative IQ scores for each participant/image were compared between CDM and AR smart glasses. ResultsOf the 38 participants, the mean age was 39 ± 1 years. 23 (60.5%) participants required corrective glasses. In terms of generalisability, participants were from 12 different countries, the majority (n = 9, 23.7%) from the United Kingdom. For eight out of ten images, the AR smart glasses demonstrated a statistically significant increase in perceived IQ (median [IQR] 2.0 [-1.0 to 7.0] points) when compared to the CDM. ConclusionAR smart glasses appear to show improvements in perceived IQ when compared to a CDM. AR smart glasses could provide an option for improving the experiences of radiographers involved in image-guided procedures and should be subject to further clinical evaluations. Implications for practiceOpportunities exist to improve perceived IQ for radiographers when reviewing fluoroscopy and IR images. AR smart glasses should be further evaluated as a potential opportunity to improve practice when visual attention is split between positioning equipment and image review.

A multi-view automatic table tennis umpiring framework

This paper presents a low-cost, portable, multi-view table tennis umpiring framework, as a viable alternative to the current expensive systems which are almost exclusively restricted to elite professional sports. Table tennis has been selected as the sport to evaluate this framework primarily because it comprises many different complex match elements, including the service, return and rally elements, which are governed by a strict set of regulations which need to be umpired. The aim is to develop novel methods to analyse and judge the legality of such key match facets, with ball detection and tracking in video frames being integral to reliably and accurately determining the ball’s position and flight path during rallies. While a low-cost option is attractive and offers several benefits, it is a technically challenging problem due to the small number and generally low-resolution cameras that are used. A novel multi-view camera setup and multi-agent system (MAS) framework is presented, which comprises computationally lightweight agents which detect and track the table tennis ball, create a 3D representation of the flight path of the ball, predict the ball’s trajectory, and identify and analyse key facets in a table tennis rally. The MAS correctly detects all state transitions in seven test table tennis sequences with minimal latency and while the processing rate of a standard computer may be unable to analyse long rallies in real time, the potential of running the MAS on a parallel architecture is a propitious alternative. The MAS is also scalable, enabling additional camera pairs to be deployed to achieve enhanced accuracy and coverage. The framework affords the potential to reform the way matches are umpired, especially for amateur players, providing an economic and objective manner of dispute resolution, while the multi-view facility is extendible to other relevant ball-based sports. The ball flight path analysis mechanism can be exploited as a valuable training tool for skill development.

Voice Assistant and Virtual Mouse using Different AI Techniques and ML

This venture advocates a Human PC Cooperation strategy where cursor development can be controlled through ongoing camera by utilizing human hand stances acknowledgment. This strategy is an option in contrast to current procedures, which incorporate physically squeezing buttons or utilizing actual PC mouse. Instead, it manages various mouse events through camera and computer vision software and can perform any action that a standard computer mouse can. The Virtual Mouse color recognition software will continuously collect photos taken in real time and process them through a number of conversions and filters. Whenever everything is changed over, the application will utilize picture handling to extricate directions of determined variety position from changed over outlines. The process then looks at a list of color combinations, each of which corresponds to a specific set of mouse actions, and compares the current color schemes in the frames. On the off chance that the ongoing variety conspire matches, the application will play out the mouse order, which will be changed over into a genuine mouse order on the user\'s PC. Notwithstanding that creators have likewise fostered a voice right hand to further develop client efficiency by overseeing routine errands of the client and by giving data from online sources to the client since voice colleague is easy to utilize. The objective of this paper is to improve the virtual assistant's performance. In this way, what precisely is a remote helper. Software that understands natural language voice commands and can carry out tasks on your behalf is known as application software, which is also referred to as AI assistants or digital assistants. They are also referred to as virtual assistants. What is the role of a virtual assistant? The list of tasks that virtual assistants can carry out that you can carry out on your own smartphone or personal computer is constantly growing. Meeting scheduling, message delivery, and weather monitoring are just a few of the impressive tasks that virtual assistants typically carry out. Google Assistant and Cortana, two examples of previous virtual assistants, were limited in that they could only perform searches and were not entirely automated. To keep the song's control function intact, these engines, for instance, lack the ability to play and pause the song. They can only have the module to play and search for songs. In order to enhance the project's functionality, we are currently working on an automated Google, YouTube, and numerous other new tasks. Now, a virtual mouse that can only be used to control the cursor and click has been added to make the process easier.