Gongcheng Kexue Yu Jishu/Advanced Engineering Science

In this work we will study geometric conharmonic curvature tensor's characteristics Generalized Vaisman- Gray manifold with flat conharmonic curvature tensor. We use the flatness property Generalized Vaisman-Gray manifold (GT-manifold). In this paper was studied the classes generalized Viasman-Gray Manifold with conharmonic tensor. We used component of Generalized Conharmonic curvature tensor of the Viasman-Gray Manifold tensor, and Defined new Classes Generalized of the Viasman-Gray Manifold, andfind the relation between component of the Classes Generalized Vaisman-gray manifold (GT)0= (GT)3, (GT)1= (GT)2,(GT)4= (GT)7,and(GT)5= (GT)6. And proved that (GT) ̅1  (GT) ̅2  (GT) ̅3.

The composite Bi(1-x)SrxFeO3 ( x = 0.0, 0.2, 0.4, 0.6, 0.8 ) was synthesized by solid-state reaction method at 850 °C, in order to evaluate the effect of strontium on the ‎structure. X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive ‎X-ray spectroscopy (EDX), magnetic analysis (VSM), and band gap energy measurements ‎were performed by UV- visible spectroscopy.‎‏ ‏The results obtained are the formation of ‎crystalline materials of rhombohedral surfaces and change to the pseudocubic phase ‎at x = 0.4. The FWHM value ranges from 0.190 to 0.980 and the particle size decreases ‎from 36.5 nm for the pure sample to 17 nm for BSFO. The Sr+2 caused an increase in ‎Remnant magnetization (Mr) and coercive field (Hc), which lead to the magnetization ‎development of BiFeO3, UV- visible spectroscopy used to‏ ‏calculate the direct optical ‎bandgap of all samples had its value on the order of 2.4 to 2.9 eV.‎

The field of image processing has been revolutionized by Convolutional Neural Networks (CNNs), which exhibit exceptional capability in feature extraction and accurate image classification. However, the training of CNNs requires large volumes of annotated data and significant computational resources. Considering these challenges, transfer learning has emerged as a promising approach to reduce the dependence on labeled data and computational resources. Transfer learning involves utilizing knowledge gained from a source tas¬¬k to improve the training process for a target task. This technique has demonstrated considerable benefits; however, it also possesses certain limitations. Consequently, this survey explores the advantages and constraints of transfer learning, as well as the various factors that influence its effectiveness in optimizing image processing using CNNs. Additionally, the survey investigates the most recent advancements and research in the field of transfer learning specifically for image processing with CNNs. In summary, this comprehensive analysis highlights the significance of transfer learning in the context of optimizing image processing with CNNs, providing unique insights into this rapidly evolving domain.

In this era, where business execution relies heavily on information, the sooner and more accurately it is received, the better. Blockchain is ideal for information sharing because it delivers instant, shareable, and completely transparent data stored in an immutable ledger that only permitted network members can access. Practically any valuable asset can be recorded and exchanged on the blockchain network, reducing risk and cost for all parties involved. This study focuses on the implementation of payroll on the blockchain. It is important to use the right strategies to safeguard and improve payroll as well as because blockchain technology is crucial for securing the payroll system because it can transfer value as well as information. This review paper's main point is that blockchain technology can be used to reduce overhead and administrative burden, reduce tax spending, and promote openness and accountability in a variety of situations. Blockchain challenges to the payroll system and implementation issues have been highlighted. Given the quantity and complexity of blockchain issues, many of the most significant blockchain roadblocks are characteristic of any new technology's growing pains. The benefits and challenges of using blockchain technology in payroll procedures were discussed after the study. It was determined that doing so would make all employee records and employer payments matching instantly available at the minute level to various governmental organizations. At a fraction of the cost of current payroll compliance utilizing a fiat cryptocurrency, a blockchain payroll application will provide quick payroll compliance. A blockchain framework that is approved for use with payroll systems and is encrypted using the high-efficiency encryption algorithm to ensure its high security will need to be designed and evaluated.

A rise in the quantity of young blood cells in the both blood and bone marrow is the primary cause of leukemia, a malignant condition that affects the system that produces blood. This disease appears in the elderly and represents the most common type of disease among young people. Symptoms appear in the form of bleeding, bruising, a feeling of fatigue, a high temperature, and a high rate of transmission of the disease. These symptoms occur due to a deficiency of mature and normal blood cells. The cause of this disease is unknown, as there are different causes depending on the type of leukemia. The genetic factor, weather and environmental conditions have a major and major role in the causes of the disease. Where there are factors that lead to an increase in the chances of contracting the disease, such as smoking, ionizing radiation, and some chemical elements. Also, individuals with a family history of the disease also have an increased chance of developing the disease. The disease can be detected and diagnosed with a blood test or by taking a dose of bone tissue. White blood cells are capable of divided into the following categories: neutrophils, monocytes, eosinophils, lymphocytes and basophils. During this period, the authors designed a CNN to classify and detect normal white blood cells. The following two actions are taken by the system in order to identify the type and shape of a typical white blood cell. Finding the primary traits and characteristics of typical WBCs is the first step. The classification of mature WBCs according to kind is the other duty. The device will be able to detect mature and normal WBCs using CNN by comparing their properties to those of higher level normal WBCs. With the amount of data used, the accuracy of the suggested method was up to 96.78%. The most pertinent studies showing the value of machine learning and its algorithms in the detection, medical image segmentation, tagging, and classification, particularly leukemia, are presented and discussed in this paper.

In general, gas turbine reliability is a challenging subject that is influenced by several aspects such as design, maintenance, operating conditions, and ecological issues. Gas turbines are widely used in power generation, aviation, and industrial applications, and reliability is crucial in all of these applications. Gas turbine reliability is measured using a variety of strategies, including mean time between failures (MTBF), mean time to repair (MTTR), availability, and reliability increase. These measurements may be used to track gas turbine performance over time and suggest areas for improvement in design or maintenance procedures. The main objective of this work is to fit the Johnson SU distribution to gas turbine data reliability. Based on the parameters estimations of the Johnson SU function, the survival function is calculated and used as a lifetime distribution model to estimate the failure rate or MTBF and anticipate the reliability of components or systems under operating conditions. As a result, it is critical to do adequate checks and sensitivity assessments to guarantee that the results are accurate and trustworthy. The results obtained can be used to determine the goodness of fit to our specific data after analysis. Finally, the Johnson SU distribution is commonly accepted as a model for turbine reliability data.

This paper studies about subsets of Natural number set N can arise as periods of additive cellular automata in special cases and which subsets of topological space Y are the periodic points in specific case.

Railways are complex systems that include infrastructure, cars, and all the other components necessary to run efficiently and safely. In an intricate rail network, passenger and freight cars must coordinate their movements to avoid collisions. Effective management of the transport system is necessary to increase the number of trains operating on the network. The control and regulation of rail traffic and tracks are necessary for both safe operation and maximum efficiency. This powers the complex and dense network of ground and airborne equipment that the signaling system uses to monitor infrastructure and convoys and to activate and transmit instructions. A large number of different devices perform essential functions along a railway track. To give just a few examples, there are the railway shunting cabins that control the points, the devices that control the state of the train (intake temperature sensors, metering, track circuit), the light signals used to communicate with the conductors and the components that allow communication between the conductors Ground conduction system and on-board conduction system (computer interlock) (electronic equipment and ground encoders). In addition, the devices on board and on the ground communicate with the central management units through intermediate management and information points. Consequently, the development of rail systems must seek innovative ways to address these challenges. A solution can be found by configuring wireless communication as a backup for the fixed network to provide services until the system is restored, and in addition to the fixed network to allow new services to enhance existing ones.

In this article, we propose a proxy re-encryption approach to secure data sharing in cloud environments. Data owners can outsource their encrypted data to the cloud using identity-based encryption, while proxy re-encryption construction will grant legitimate users access to the data. With the Internet of Things devices being resource-constrained, an edge device acts as a proxy server to handle intensive computations. Also, we make use of the features of information-centric networking to deliver cached content in the proxy effectively, thus improving the quality of service and making good use of the network bandwidth. Further, our system model is based on blockchain, a disruptive technology that enables decentralization in data sharing. It mitigates the bottlenecks in centralized systems and achieves fine-grained access control to data.

This paper is related about the concept of decycling set and decycling number of graph G. any subset of set of vertices F⊂V(G) is known as decycling set of graph G if the subgraph G-F is cycle free graph or acyclic graph. Then cardinality of smallest decycling set is known as decycling number of graph G and it is denoted by ∇(G). Here we will study the decycling number of the Cartesian product of a cycle graph (C_n) and path graph (P_2) ∀ n≥3. Graphs C_n□P_2 known as family of prism graphs and it is denoted by Y_n. Moreover, we will find a vertex set F_n whose deletion in prism graphs (Y_n) gives acyclic graph. This paper also proves that the decycling number of prism graph is

Background: Novel Corona Virus is increasing day by day; it is needed to identify the formal techniques and innovative trendy approaches. The responsive effort in short times is needed. Objective: The aim of this study was to consequently, display things that recognize COVID-19 pneumonia accurately. This examine work is pointing to particularly offer assistance with the conclusion of COVID-19. Methods: The proposed work is centred on the headway of an AI-based examination of CT pictures of the cutting edge crown infection. The proposed system classifies CT pictures utilizing balanced Resnet appear name ResNext. The Convolutional layers of particular sizes were utilized in each of the confined ways of Resnet illustrated. In Resnext, 32 channels are bound together at the same bottleneck and convolve them. This made it conceivable to perform indistinguishable changes to gather convolution in 32 bunches, which compares to the initial 32 courses. The proposed show effectively separated between viral pneumonia and COVID-19 influenced lung CT pictures. The accuracies of DenseNet, mobileNet and VggNet are 90.91,75.24 and 35.75 respectively for testing as shown in table 4. Results: False positives are identified among normal images, Covid effected images and viral pneumonia images. Also the validation loss and validation accuracy of the training process is to be processed and observed. The training loss is also calculated and observed. The training accuracy and validation accuracy both reached 100%. The final testing accuracy of the proposed model is 100%. The performance comparison of existing methods with the proposed method is also observed. The proposed method obtained better classification accuracy when compared to the existing deep learning models DenseNet, mobileNet and VggNet. The accuracies of DenseNet, mobileNet and VggNet are 89.3, 72.72 and 30.30 respectively for training. Conclusion: The clinical execution of the PCR test for COVID-19 pneumonia is not accurate. This paper points to assist make stride the AI examination stage for COVID-19 pneumonia investigate and make strides the precision of AI choice and judgment. The proposed demonstrate gotten an exactness of 100% in dispensing with wrong positives delivering accurate COVID-19 detection.

Present study exploring the influence of Dufour effect on a non-linear heat and mass transfer flow past a stretching/shrinking sheet prescribed with variable heat flux in the presence of heat source and constant suction. The fluid viscosity and thermal conductivity are assumed to be inverse linear functions of temperature. A numerical solution for the system of non-linear momentum, energy and concentration equations are worked out by adopting Runge-Kutta shooting method. The consequences of both viscosity and thermal conductivity parameters along with Dufour number and Magnetic parameter on the flow are presented and discussed graphically.

The corrosion behavior of carbon steel type A106 was studied in the field which was used in pipelines transporting crude oil from fuel tanks to the subsequent refinery processes in the North Refineries Company in Iraq by calculating the weight loss method of samples to measure the rate of weight loss in mile per year (MPY) unit. The present study uses different forms of Iraqi crude oil from different places: Qayyarah crude oil, Kirkuk crude oil, and Basra crude oil. The results show that the corrosion rate increased at the rising content of water, salt content, and flow rate of crude oil, and Qayyara crude oil has the above rate of corrosion of carbon steel than Basra and Kirkuk crude oil.

In this work, waste plastic oils were converted into a sustainable alternative fuel utilising the transesterification procedure in the presence of a catalyst and alcohol. In compliance with ASTM, the essential qualities of biodiesel produced were analysed. Fuel mixes like D100 (Diesel fuel), P20D80, P40D60 and P100D0 (plastic oil biodiesel) was obtained. The single-cylinder diesel engine employed in this experiment was completely computerised and used direct injection. With CR18, P20D80 demonstrated a reduction in braking torque of 1.3% and cylinder temperature of 0.7%. At full load, less exhaust gas emissions, including smoke emissions, were recorded. P20D80 mix exhibited more favourable engine characteristics, and P20D80 may be suggested as a possible renewable alternative fuel for conventional diesel when used in engines without modification

In the field of medical diagnosis and treatment, medical images are essential, and their security is critical to ensuring their validity and integrity. Watermarking has become a potential method for protecting digital medical images as their use has grown. To enable copyright protection, tamper detection, and content verification, watermarking entails putting an undetectable mark onto the image. Throughout the literature, a variety of watermarking methods have been suggested, each having advantages and drawbacks of its own. Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Redundant Discrete Wavelet Transform are just a few of the different watermarking methods covered in this paper's thorough review of their applications in medical image security (RDWT). The performance of these algorithms is also compared in this work using a variety of measures, including Peak Signal-to-Noise Ratio (PSNR), Normalized Correlation (NC), Structural Similarity Index (SSIM), and Bit Error Rate (BER). As a result of its great robustness and imperceptibility, PCA is the technique that works best for watermarking medical images, according to the results. The performance of watermarking approaches should be assessed using the right metrics and benchmarking tools, as this paper clearly emphasises. The results of this study can help practitioners and researchers in the field of medical image security select the best watermarking method depending on their unique needs.

Since the changes brought on by information technology applications are pervasive in all parts of contemporary firms, the current research has provided prospects for using information technology for digitally altering the modern businesses. In order to transform and accelerate their business processes, modern businesses have been focusing on integrating cutting-edge information technologies, such as analytics, mobile, social, cloud computing, and big data. This is according to a thorough conceptual review of the literature on the importance of information technology in management. The ability to strategically manage modern business processes digitally is mostly defined by a clear digital strategy that is backed by managers and leaders who support an organisational culture. However, integrating various aspects of this new information technology into modern enterprises also presents some significant difficulties, such as issues with data security, a lack of control, and interoperability with already installed IT infrastructure. There has also been discussion of the managerial implications and future research objectives.

In biometric systems, securityand privacy present significant difficulties. The confidentiality and integrity of biometric data should be secured from all attacks, especially those that target sensitive data. This paper offers a thorough analysis of several physiological biometric methods. To increase the reliability of authentication, this study examines a multimodal biometric system using a combination of three biometric traits: iris, fingerprint, and face.Initially, a watermarking technology has been employed to improve the security for database. LSB Watermarking technology has been employed to Embed Face, irisand Fingerprint images to improve protection in biometric recognition system. In addition to this author have developed Iris and Face traits using LBP and PCA with SVM. Further, Iris-Face-Finger Print traits have been used to develop a more authenticated multi model System with LBP and PCA separately. In the proposed method privacy protection has been provided for the data base using LSB approach. Our proposed method results with an accuracy of 88% for the combination of LBP and SVM with Iris-Face traits.Similarly, 89% is obtained using PCA and SVM, and 92% using LBP and SVMfor Iris-Fingerprint-Face traits respectively. Security level of the biometric system is measured through the Equal Error Rate and is observed as 0.08 for Iris-Fingerprint-Face traits using LBP.

In biometric systems, securityand privacy present significant difficulties. The confidentiality and integrity of biometric data should be secured from all attacks, especially those that target sensitive data. This paper offers a thorough analysis of several physiological biometric methods. To increase the reliability of authentication, this study examines a multimodal biometric system using a combination of three biometric traits: iris, fingerprint, and face.Initially, a watermarking technology has been employed to improve the security for database. LSB Watermarking technology has been employed to Embed Face, irisand Fingerprint images to improve protection in biometric recognition system. In addition to this author have developed Iris and Face traits using LBP and PCA with SVM. Further, Iris-Face-Finger Print traits have been used to develop a more authenticated multi model System with LBP and PCA separately. In the proposed method privacy protection has been provided for the data base using LSB approach. Our proposed method results with an accuracy of 88% for the combination of LBP and SVM with Iris-Face traits.Similarly, 89% is obtained using PCA and SVM, and 92% using LBP and SVMfor Iris-Fingerprint-Face traits respectively. Security level of the biometric system is measured through the Equal Error Rate and is observed as 0.08 for Iris-Fingerprint-Face traits using LBP.

It has been observed that harmonics play a major role in reducing the quality of power in industrial and utility power systems. These harmonics are activated by the growing use of nonlinear loads connected to the power system. Passive and active filters (shunt and series filter) were traditionally used to generate the power efficiency. However, they suffered from resonance problems, fixed compensation and other PQ problems. To minimise these problems, we therefore go for controllers such as PID, Fuzzy and Neuro Fuzzy controllers. In order to implement the proposed control technique, and is tested using various voltage conditions, such as sag swell and unbalanced. By comparing research with traditional control methods, the efficacy of the proposed neuro fuzzy-based SVPWM controller is analysed. The main objective of this paper is to reduce the total harmonic distortion and remove power quality issues and maintain stability in load voltage by using three methods and is compared in three cases. They are the PWM based PID and fuzzy logic controller which are compared with SVPWM based Neuro fuzzy controller. One of the new metaheuristic algorithms is the Modified Firefly Algorithm (MFA) is used for the optimization problems to yield better PID gain parameters. In this algorithm, randomly generated solutions will be considered as fireflies and the brightness is assigned depending on their performance on the objective function. The basic rule used to construct the algorithm is, a firefly will be attracted to a brighter firefly and if there is no brighter firefly it moves randomly generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. The aim of attractiveness is modified in such a way that the effect of the objective function is modified which helps in finding the best solution with smaller CPU time and is implemented in MATLAB/simulation.

Facial expression recognition (FER) is a common investigation phenomenon, resulting in several computational vision tasks like super-resolution reconstruction, image generation, image translation, and video generation. FER has led to many challenges, which have resulted in numerous technological advances in AI and computer vision. The major steps of FER include preprocessing, feature extraction, and classification. Several face detection techniques have been proposed, including the eigenspace technique, Viola-Jones, and adaptive skin color algorithm among others, and the methods have been designed centered on the Haar classifier, contour points, and Adaboost. The key methods used in feature extraction include principal component analysis, Gabor feature, local binary patterns (LBPs), and active appearance models. Various classification algorithms are deployed in this phase, including directed Line segment Hausdorff Distance (dLHD), K-nearest neighbors (KNN), Support Vector Machine, Hidden Markov Model (HMM), Hidden Conditional Random Fields (HCRF), Online Sequential Extreme Learning Machine (OSELM), Learning Vector Quantization (LVQ), ID3 Decision Tree (DT), Multilayer Feed Forward Neural Network (MFFNN), Bayesian Neural Network, Convolution Neural Network (CNN), Deep Neural Network (DNN), and Deep Belief Network (DBN). Notwithstanding the recent development, FER is still a challenging task influenced by several factors like pose variation, multifaceted background, illumination, and pose variation.

The experimental work in this paper compares the performance of a solar collector with triangular tubes placed over grooves of a V-corrugated absorber plate and a solar collector with circular tubes placed on a flat plate under the same environmental conditions and at four flow conditions (2,3,5,7) Lpm. The results were, The solar collector with triangular tubes placed over grooves of V-corrugated absorber plate is more efficient in thermal conductivity than the solar collector with circular tubes placed on the flat plate, because rises temperature of the triangular riser tubes result of global warming in the grooves of the corrugated plate, also because the surface area of the corrugated plate V with grooves is greater than the surface area of the flat plate, and as a result, the amount of solar radiation falling on it is greater than the amount of solar radiation falling on the flat plate. Additionally, the triangle-shaped tubes have a larger surface area of contact with the V-corrugated absorber plate than the circular tubes do with the flat plate. This enhances the heat transfer from the V-corrugated plate to the triangle-shaped tubes, which increases the heating of the water used. Increasing the water flow in the pipes of both solar collectors increases the amount of heat exchange between the pipes and the water inside them because there are more water passages inside the solar collector when the flow is high, which increases the water's ability to absorb heat from the sun's rays. This raises the solar collector's efficiency. Four water flows, each at a rate of 2, 3, 5, and 7 l/min, were used in this study.

Research purpose: under the environment of modern big data technology, through the application and development of artificial intelligence, 3D modeling and other computer technologies; Bring the design field of our country from two-dimensional to three-dimensional development; By combining the traditional folk plastic arts and cultural background of our country and integrating national art into the current interior decoration design, even if the development of the design field has more innovative elements, it also provides a new channel for the inheritance and development of our folk culture and art; This paper will analyze and study the application of big data technology in indoor decoration and the integration of folk art modeling from the perspective of sustainable development, and understand the advantages of big data technology application and the impact of inheriting folk art.Through the overall effect of folk modeling art in interior decoration under sustainable development environment and ordinary environment, as well as its performance analysis and coupling under different technologies, it is statistically significant, It is obvious that the analysis and research in the environment of sustainable development is better than the previous ordinary environment. In the environment of sustainable development, interior decoration design based on big data. With the help of computer-aided integration of information technology, the combination of data and design breaks the inherent thinking mode of traditional design.Through the combination of network communication technology, As well as the virtual scene of VR technology, it can realize three-dimensional dynamic image, and can observe all corners of the space in an all-round way, so that people can have a clearer understanding of the decorative design style,computer technology, etc., and the full integration of folk plastic arts, the ideal effect of interior decoration design can be met to a great extent, making interior decoration show unique art, Help promote its development.

Sol-gel auto-combustion technology was used to create Mg-Co-Cu nano ferrites that are doped with lanthanum and have the chemical formula Mg0.5Co0.25Cu0.25Fe¬LaxO4 (x = 0.0, 0.025, 0.05, 0.075, 0.1). At 1000 °C, all of the samples were sintered. The creation of the cubic spinel structure can be seen in all samples, according to the X-ray diffraction (XRD) investigation. As it turns out, the lattice constant fluctuates with the presence of La3+ compounds, increasing when more La3+ ions are added. The Fourier transform infrared (FT-IR) measurement that was obtained supports the creation of the spinel structure as well. The higher frequency v1 is edging a little bit closer to the high-frequency side with the rise in La. Particles with spherically cubic crystallite shapes are visible in pictures obtained using FESEM. Energy dispersive X-ray analysis was used to confirm the presence of components, or constituents, such as Mg, Co, Cu, Fe, and La. (EDX). La3+ doping was accomplished, and it was determined. By using X-ray photoelectron spectroscopy, the binding energies of La, Mg, Co, Cu, Fe, and O were determined. The impedance study verified the short crystal size, resulting in a grain border contribution more significant than the grain contribution. Growing frequencies resulted in a drop in the dielectric constant and dielectric losses. When the sample's La doping content was at x = 0.025, the sample's magnetic characteristics were at their best. This suggests that using the right amount of doping can enhance the samples' magnetic characteristics. The substitution of non-magnetic La3+ ions for magnetic Fe3+ ions (5μB) results in a decrease in the magnetic moment, which lowers the net total magnetic moment.

In this project, a novel approximative full adder is suggested and used in the multiplier's reduction steps. Here, four designs for 8-bit multipliers are looked at for use with the approximate 4-2 compressors that already exist and the approximate full adder that has been suggested. For error-resistant applications, approximate computing can lessen design complexity while enhancing performance and power efficiency. In the majority of multimedia applications, we may learn important things from marginally inaccurate outputs. As a result, we are not required to produce precise results. This short paper shows a unique way to change the logic at the gate level to approximate full adders and take advantage of the loosening of numerical precision. The standard full adder's sum phrase is modified to simplify an area. The efficacy of the proposed solution is developed and simulated using Xilinx Vivado.

We investigate the power flux relationships of guided waves in a three-layered slab waveguide with chiral metamaterial. This waveguide model contains an interfaces between adjacent layers. The graphene was used for this purpose as a result of its excellent optical properties. The interfaces thickness is 1.02nm , where three monolayer graphene were used. Power equations were obtained as well as Poynting vector for this type of structures and graphene properties. The physical explanations for such phenomenon have also been presented. Simulation results have validated our theoretical analysis. The power profiles in waveguide regions are potted and discussed. The results show unexpected characteristics including the absence of zero mode caused by the graphene interface in waveguide structure. The even modes do not provide any support for the fundamental mode for any parameters used.

Big Data analytics is employed to derive insightful information, such as occult patterns, obscure relationships, market trends, and consumer preferences. Better decision-making and the prevention of fraud are achieved through the use of big data analytics. One of the most well-known sectors where data analytics is having a big impact is healthcare. Healthcare analytics offers the ability to lower treatment costs, anticipate epidemic outbreaks, prevent infections, and enhance quality of life. This paper presents a comprehensive evaluation of several current studies on big data analytics and healthcare analytics. Big data architecture and big data technology are also discussed in this paper.

A fusion of medical images can be used to enhance the medical diagnosis and treatment of brain pathology. Image fusion has become a popular tool in medical applications to improve image quality. In applications involving medical imaging, image fusion is essential. By assisting radiologists in identifying abnormalities in CT and MR brain images. On many fused images with greater information, a comparison analysis was performed.Medical image fusion in multimodality images like MRI and CT images will enhance the exactness of the image for identifying tumour cells in the brain, as well as provide doctors and clinical treatment designing systems with more information. This paper reviews the comparative analysis of medical image fusion techniques for fusing MRI and CT images to clearly determine brain tumours and help the physician make a better diagnosis. All performance metrics taken into consideration here. The experimental findings showed that the suggested strategies produce better viewing of fused images and produce superior outcomes to a number of currently used conventional algorithms.

Because of the quick development of organization information, the validness and dependability of organization data have become progressively significant and have introduced difficulties. A large portion of the strategies for counterfeit survey recognition start with printed highlights and social elements. Notwithstanding, they are tedious and effortlessly identified by false clients. Albeit the vast majority of the current brain network-based strategies address the issues introduced by the complicated semantics of audits, they don't represent the understood examples among clients, surveys, and items; also, they don't consider the convenience of data with respect to ne-grained perspectives in recognizing counterfeit audits. In this paper, we propose a consideration based staggered intuitive neural network model with multilevel imperatives that mines the staggered implied articulation method of audits and coordinates four aspects, to be specific, clients, survey messages, items and fine-grained perspectives, into audit portrayals. And predict the fake reviews using Navie bayes, SVM and Logistic regression algorithms. Finally, we show the simulation results of these models in detecting the fake reviews.

This study has two objectives; first is to investigate how the nature of the macro-environment (political and economic) vary in different geographical contexts, and second is to explore if different strategic thinking dimensions impact the bank’s performance. The current research adopts a qualitative methodology using a comparative case study method. Purposive sampling and semi-structured in-depth interviews have been used to obtain data from 50 bank executives; 33 of whom are Malaysians and 17 Palestinians. Data analysis was conducted using Atlas Software. The finding of this study shows that executives’ strategic thinking dimensions are influenced by the macro and micro-environment, which in turn impacts the bank’s performance. The executives’ strategic action in an uncertain environment varies based on the type of bank. In Commercial banks (local and foreign), executives understand and evaluate the external environment, while local Islamic bank executives focus on following up and monitoring all the changes. In addition, in a stable environment, the executives’ strategic actions for foreign banks (commercial and Islamic) understand and evaluate the environment. This indicates that environmental conditions influence the banks’ performance. The (STEM) directs the executives’ strategic action, which impacts the bank’s internal process, employees’ perspectives, and product development and growth, directly affecting the bank’s performance, while the (STDM) guides local Islamic bank’s executives’ strategic action and focuses on customer satisfaction.

In today's world, the rapidly converging fields of electronics textiles make it possible for sensors to be seamlessly and extensively incorporated into textiles, as well as for conductive yarn to be manufactured. A new age in retail may be on the horizon thanks to the possibility of smart textiles, which can interface with smartphones and interpret biological data such as a person's heart rate, temperature, respiration, stress, activity, speed, or even hormone levels. In this work, a study is performed on wearables and garments to create e-textilesusing machine learning techniques such as Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF),Decision Tree (DT) and ensemble classifiers.Some parameters are taken into consideration such as Body Mass Index (BMI), Mental health data, sleep time data as health monitoring data,and race and differ walkingas physical activities to evaluate performance of suggested method.Linear Discrimination Analysis (LDA) is used to dimensionality reductionof health monitoring data and then general health is predicted. Machine learning techniques and ensemble classifiers are applied on physical health activities, to predict physical health. Overall results of the suggested method areestimated in terms of accuracy, recall and F1-measure.Statistical and investigation results shows that the ensemble classifiers obtained the highest accuracy (99.15%), recall (99%) and F1-measure (99.5%) than all other methods.

In today’s world healthcare system is getting more importance because of our life style and food habits. According to the World Health Organization report, approximately 32% of death rate in worldwide is due to Cardio-vascular disease. Coronary heart disease, Cerebro vascular disease, Rheumatic heart disease etc. are comes under this cardio vascular diseases. In healthcare systems, past years many machine learning techniques are deployed for analysis and prediction. In order to enhance and improve prediction accuracy, now a days, many deep learning techniques are providing more promising results in pattern recognition which in turn improves prediction accuracy in the healthcare systems. There are three components in the deep learning pipeline for cardiac ultrasonic imaging, they are data collection and preparation, network selection, training and evaluation. This survey paper an attempt is made to explores various deep learning techniques namely Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks(RNN) for early disease prediction. Also explored about different types of data sets and feature extraction techniques involved in this prediction process. The main contribution of this survey paper, for past one decade different deep learning techniques involved in predicting Cardio-vascular disease and comparison has been made based on their accuracy with their dataset involved and feature extraction techniques used.

Video was taken for research purposes only at various locations along national and state highways in Tamil Nadu, India. Video is comprised of image frames. The image frames are used to object detection then result of the detection counts are abruptly high because the image frames are nearly same. Research problem is Avoiding video files for object detection Why because the image frames are almost same, image processing time is high and required high configuration system. The video file transform to distinct image frames by python script.

A number of more recent discoveries in microbiology have made reliable identification of nano-biomolecules and extensive analyses of them necessary. A variety of proteins, including DNA, biotin-streptavidin, amino acids, as well as many types of bacteria and viruses, must be found and analyzed in order to fully comprehend any odd behavior occurring inside of live cells. Rapid testing and detection are essential steps in preventing undiscovered biohazards from eradicating the human race and other terrestrial living things. Since many decades ago, developing an accurate, affordable biosensor has been a struggle for scientists [1]. When compared to pricey laboratory-based sensors and detection methods, FET-based lab-on-chip nano biosensors appear to be a promising substitute. It is significantly more dependable than conventional bulk sensors because of its size, affordability, low power consumption, resilience, faster response time, and better sensitivity [1]. Due to their precision, adaptability, and compatibility with embedded systems, dielectrically modulated FET biosensors with Nano cavities are emerging as a promising research area that can yield useful data on bio-analyses. As an alternative to conventionally doped TFET devices, using a charge plasma SiGe-heterojunction double gate TFET, a label-free biosensor can be produced, bypassing the need for conventional semiconductors, which require a large thermal budget and are susceptible to random dopant fluctuations (RDFs). The effect of changing the dielectric constant (k), the positive and negative charge density, the gate work function, and the cavity size has been investigated to better understand how these factors affect the performance of the proposed biosensor. These parameters modify the biosensor's electric characteristics, improving detection [2]. There is also discussion of how these factors influence the device's drain current, electric field, surface potential, sub-threshold swing (SS), insulator-to-metal film (ION/IOFF) ratio, and electron tunneling rate (ETR). The sensitivity of the drain current in the proposed biosensor is also investigated. There is no restriction on whether or whether the proposed structure is used for charged or neutral molecules. Under lower supply voltages, it is discovered that the SG-DM current JLFET's sensitivity is high, measuring 1.2 *10^3, with a potential sensitivity of 1.4 V. A result, the SG-DM [2] JLFET exhibits good application potential while consuming little power and having a high sensitivity.

In present era, the work life balance has become a topic of study for the organizations as well as for the research scholars. It is most challenging issue faced by persons who has to work for the organization, especially for the Indian women now-a-days. As the family size has become nuclear, and due to such inflation it is necessary to work for the women. Also a woman is also educated and she wants to be stand on her feet. But a woman has to play so many responsibilities. She has the responsibilities towards her home and work as well. Sometimes it is difficult for her to manage both the responsibilities at a time. This paper has discussed the problem of Work life balance of a women. This study is emphasized to make appropriate policies by the HR of an organization for the female employees because it helps in their work satisfaction and ultimately it results in excellent output.

An unconventional interface is described whereby infill panels are separated from the plane Reinforced concrete (RC) frame employing an adaptive layer of Pneumatic interface material whose interface property can be changed by varying the interface pressure. Using fully infilled RC frames subjected to cyclic in-plane load within the elastic limit, the theorywas able to verify experimentally. The results from tests show the interface pressure optimization and the pressure pattern for the better ductile behaviour of the infilled frames. Conclusions Up to modest drifts, the suggested method has an excellent perspective in decreasing the interaction between the structure ofthe infill and, by extension, the disintegrationof the infills. The interaction exists once the interface is compressed and at increased drift. In this way, an infilled frame with a Pneumatic interface can act as a bare frame and infilled frame with interface pressure variations.

Researchers have been working on quite normal shape frame structures such as square and rectangular frame structures that are easily adaptable for many centuries. High-rise construction is becoming more common these days. As a result, in the case of high-rise buildings, regular shapes such as square and rectangular frame structures are insufficient in resisting lateral loads such as wind and seismic loads. To resist more lateral force for superstructures, a new geometry of frame configuration known as trapezoidal frames is adapted. Under static loading, a two-dimensional single-bay single-story reinforced concrete trapezoidal infilled frame with cement mortar as an interface and optimization of different percentages of openings in the infill were investigated. The paper specifically discusses the results such as displacement, lateral stiffness, bending moment, shear force, lateral load, and stresses in the infill wall.

Rapid spectrum usage in wireless networks may reduce energy efficiency, necessitating cognitive radio networks that are more energy-efficient than conventional ones. A decrease in the available spectrum's bandwidth led to the development of cognitive radio networks, which are now widely used for data transmission. In order to boost sensing efficacy and system throughput, current research ignores energy economy and handoff delay in favor of handoff decision and cooperative spectrum sensing. The energy consumption of the sensing process may be made more efficient. A threshold approach based on primary user traffic patterns is offered for spectrum mobility control. Using a threshold method, the values for probabilistic stay-and-wait and QoS handoff are calculated. In addition, a method for selecting the channel with the maximum throughput and least amount of energy consumption is developed and shown. The recommended technique reduces false alarms and miss detection while maintaining handoff delay and enhancing throughput and energy efficiency. This approach enhances the throughput, energy economy, sensor performance, and handoff time. miss-detection.

Bearings are an integral part of a bridge. They play a pivotal role in ensuring the optimum performance of the bridge. This study attempts to present an Indian perspective on the significance and functionalities of bridge bearings. It provides an overview of bearing design provisions as adopted by Indian Standard Codes, with a brief background on improvements incorporated. It covers the different facets of bridge bearings and provides an account of prominent issues pertaining to the domain. The recommendations of different researchers and the bottlenecks and prominent deductions they proposed, have all been compiled in this review. The different types of bearings, along with their relative characteristic comparisons, have been discussed, and the importance of bearings on the structural action and efficiency of the bridge has been established.

This research aims to investigate the frequency of magnetic impulses that disrupt the bio-electric signals produced during the cellular division of the Rift Valley Virus (Rvfv). For further analysis of the results, the experiment was expanded to an in vivo study in which mice were infected with Rvfv and were whole body exposed to square amplitude modulated waves (QAMW) that caused inhibition to the Rvfv. Previously inhibited Rvfv infected another group of animals, then the histological and biochemical of the liver were investigated for all the animal groups. The molecular structural changes in the liver were determined using dielectric relaxation tests in the frequency range of 100 kHz to 4.5 MHz. The results showed that after being exposed to the resonance frequency of 5.2 Hz Rvfv for 30 minutes, there was a highly significant reduction of cellular growth for Rvfv in addition to noticeable alterations in cellular morphology. When animals were infected with the Rift Valley virus (Rvfv) and subsequently exposed to QAMW throughout their bodies, histological and dielectric relaxation studies for the liver in mice revealed a considerable increase in liver health compared to the infected and non-exposed group. Furthermore, compared to animals with the untreated virus, the liver of animals infected with previously treated Rvfv (QAMW) displayed a significantly substantial decrease in cellular damage. Conclusion: Rvfv therapy at 5.2 Hz (QAMW) affects the biological activity and structure of the receptor, and it offers a promising way of regulating Rvfv activity in vivo applications.

In this Study, the weight percentages of different reinforcements are added in Nylon66 parent material in order to enhance its mechanical, tribological properties of pure Nylon66 Composite. Test specimens of pure Nylon66 with different weight percentages of reinforcements are prepared by using an injection molding machine. Various tests are conducted on new polymer matrix composite was carried out the tribometer or pin on disk setup is done to study the wear charactristics of the material. The obtained result reveal that the mechanical properties as well as tribological properties of the newly prepared Nylon composite are significantly increased by the weight percentage of MoS2 (molebdenum disulphide) reinforcement material , here pure Nylon indications the lowest tensile strength as compared to newly formed Nylon composite material. This paper summarized, the parametric influence of Reinforcement, temperature and load on wear rate of nylonn 66 material with MoS2 reinforcement. Response surface methodology (RSM) and Taguchi method is used for modelling and optimization. ANOVA has been carried out to identify importance of the operating parameters on the performance characteristics considered. Further the verification experiment has been carried out to confirm the performance of optimum parameters. The results from this study will be useful for selecting appropriate set of process parameters has been selected. The analysis of variance (ANOVA) has been used to determine effect of each parameter on wear rate so Finally, the confirmation test has been carried out to compare the predicted value of wear rate with the experimental value

In this paper we propose a class of error correcting codes called ”Real Unit Vector Codes” for detecting and correcting some special type of error known as ”Neighbour-Interference”. This class of codes can be used in a communication system in which messages are represented by arrays of real vectors. We present the encoding and decoding schemes for this class of codes. An example of a real unit vector code is also constructed.

In this work, we study various features of derivations on prime and semiprime rings. These rings are important in mathematics. We have shown, amongst other things, “that if R is indeed a semiprime ring, I denote a nonzero two-sided ideal of R, and f and g are derivations of R,” then they must meet the following condition: "if F(x)y+yg(x)=(0)for all x,y∈I,thence F(u)[x,y]=[x,y]g(u)=0" for every value of x,y∈I. In specifically, f and g are responsible for mapping I onto the middle of R. “If R represents a noncommutative prime ring, thence f=g=0 holds true for R. This statement may be thought of as an analogue of the Posner Lemma for a pair of derivations that meet this identity.”

In recent years, deep learning techniques have revolutionized the way natural language processing (NLP) is being utilized to accomplish many real-world language generation tasks, like machine translation, text summarization, chatbots, and dialog generation. Despite this, there are still many challenges to be addressed, one of which is handling the missing data. In the real world, there are many instances of incompleteness and missing values in text datasets resulting from unrecorded observations, limiting the usefulness of language generation models. In most cases, imputation techniques replace missing data by substituting some values to preserve information within the data. Unfortunately, most imputation methods operate on numerical and are rarely applied to textual data, which remains a challenging problem. This paper proposes a deep learning-driven text imputation model that determines the probability of missing words in a sentence based on preceding and subsequent terms. A sequence-to-sequence language model is developed that uses a recurrent neural network and attention mechanism. On the other hand, the study applies an iterative search optimization algorithm to a trained model to predict the most likely words and insert them into the missing place that exists anywhere in the given input text data. The result indicated that the proposed imputation scheme consistently and efficiently replaced missing words with appropriate words in the given missing text. The proposed model is suitable for many real-world language generation and text analytics tasks.

The present work is on the mechanical study of hybrid composites made out of s-glass fiber being combined with carbon fiber as synthetic reinforcements reinforced with thermoset polymer matrix; namely epoxy by hand layup technique of required laminate thickness suitable for testing as per ASTM standards. The mechanical behavior of the hybrid material formed with the differently stacked fiber layers at required proportion were maintained for the tests. The physical and mechanical properties such as density, tensile, impact, flexural and hardness properties of the hybrids composites were determined after being subjected to various mechanical loads under controlled standard condition. The test results, reveals that the combination of 4K-2S-4 K-E-HC hybrid in the material system has higher hardness, density, tensile, and impact properties, but the material configuration of 3K-2S-3K-2S-E-HC exhibited good flexural strength. S-glass fiber being employed in the hybrid has been quite increased the mechanical abilities. SEM pictures taken show the morphology of failed sample under tensile loading indicating fiber breakage, fiber pull-outs and matrix failure across the fiber interaction.