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There are many measurable dimensions for products in industries, and their quality should not deviate too much from the established norm. Therefore, a big part of the consignment’s acceptance is determined by the variance of distributions. In the production of surgical or critical care equipment, measurable dimensions are very important, since each part of the equipment should meet the indicated specification with tolerable variance. Therefore, a procedure for constructing a combined (mixed) sampling plan with a variance measure is provided in this module. This sampling plan supports the medical device engineer in establishing the target dimensions for the quality control of medical instruments because many variations in dimensions result in heavy losses for manufacturers. The relational complete chain sampling plan (RCCSP) is utilized in the II phase inspection to have a significant impact on production quality. The performance measures, algorithm, and design of the mixed sampling plan are provided. The plan is indexed through AQL. In order to choose the plan easily, the table is set up with an example.

Kidney stones are renal calculi that are formed due to the collection of calcium and uric acid. The major symptom for the existence of these renal calculi is severe pain, especially when it travels down the urethras To detect these renal calculi, ultrasound images are preferable. But these images have speckle noise which makes the detection of stone challenge. To obtain better results, Semantic Object Region and Morphological Analysis (SORAMA) found to be productive. First scanned image undergoes noise removal process Later the image is enhanced. Detection of Region of interest (ROI) in the image is done. Later it undergoes Dilation and Erosion were a part of Morphological analysis which produces a smoothening effect on the image. From the smoothened image, the stone is detected. If the stone is not detected then it again undergoes noise removal technique and the whole process is repeated until the smoothened image with the stone is detected. This novel research paper will be a boon to medical patients suffering from this disease to be detected and diagnose at a very early stage.

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

In the modern evolution, WSN (Wireless Sensor Network) incorporated with data aggregation platform which involves stimulating research area with various modern upgradation of AI (Artificial Intelligence). Many types of research are carried out by undertaking variety of Deep Learning Network and Fuzzy based data aggregation techniques in the interpretation of Wireless Sensor Circumstance. The focal theme of the proposed research paper is to analyze the present concentrated work on Artificial Intelligence-accompanied data aggregation paradigm in wireless communication by elaborating the integration framework. By this proposed AI with data aggregation wireless sensor system has upgraded the innovation with high empowering pillars in analyzing the data with more processing and interpretations. Since it also improves the data transmission rate by providing more security and encryption schemes to the preprocessed data storing and streaming ton the bandwidth channels. This paper enumerates the AI contribution on the computing platform with the various advancement of blockchain technology schematic framework and further its case study experimentation on the aero-engine applications. Further experimental results are provided with numerical analysis by showing the traditional and advanced- Long term Memory technique prediction results by taking aero-engine wireless sensor network environment through MATLAB simulation.

The metamaterial absorber intends the structure of the solar cell to boost its internal parameters like the short-circuit current density (Jsc), Voc and efficiency. In this work, A novel single band polarization insensitive metamaterial (MTM) absorber is designed and investigated for solar cell energy harvesting applications. The proposed MTM unit cell is made of three layers named as glass bottom layer, layer 1, layer 2 and a top metal conducting layer. All these layers are stacked on a glass substrate with a thickness of 2.5 nm. The proposed MTM unit cell and its variations makes it possible to have a higher absorption rate of greater than 75% across the full range of visible spectrum. By analyzing the unit cell, it was found that it delivers a tunable character and maximum absorption of >75% over a wide range of frequency (534–672 THz) in the visible regime. The parametric study of overall size of the MTM absorber was analyzed over the frequency range from 450 to 750 THz. This design responds to a near perfect absorption of 99.99% at a resonance frequency of 560–570 THz and also has >90% absorption between 570 and 650 THz frequencies which is almost half of the visible range. Based on the similar works in the existing literature it has been found that the proposed MTM unit cell design was found out to be the best for high-efficiency solar cell energy harvesting applications.

Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks.

Worldwide, new devices in every sphere of the walk are emerging daily, and a more significant percentage of them are software-driven. Consequently, the software development process is prone to errors, bugs, and frequent changes to the requirements and the software leading to abnormal deviation in time to deliver. Another aspect being considered is the security of the software at the time of (Software Development Life Cycle) SDLC. As a part of secure software engineering, the model transformation involves the information security aspects in the early phases of SDLC. Hence, Automation of the software development process, though not entirely, is inevitable. In this research, platform-independent models are generated with security attributes such as Integrity, Privacy, Security Audit, non-repudiation, and authentication. A template-based source code generator is used to produce the structure of the source model. The Secure Business Process Model (SBPM) has a set of Unified Modeling Language (UML) artifacts such as analysis level classes and sequence diagrams with security attributes by taking the activity model as a source. Security requirements are associated with elements extracted from the source model. Structural codes for the source model with security-enabled members are generated.

Fraudulent online transactions have caused significant damage and loss to individuals and companies over a period of time. There has been an increase in online fraud with the progression of state-of-the-art technologies and worldwide communication. The design of efficient fraud detection algorithms is critical for reducing these losses. Machine learning and statistical techniques play a vital role in the detection of fraudulent transactions. Fraud detection model implementation is particularly challenging due to the lack of data, sensitive nature of data, and the unbalanced class distributions. It is tough to draw inferences and build better models due to the confidentiality of the records. In this paper we aim to focus on the problems: i) the role of sampling in the presence of class imbalance (i.e., nonfraudulent transactions are more in the percentage of the total transactions), ii) building and analyzing various machine learning models, iii) to assess and validate the performances of different fraud detection techniques. This paper has research directions toward applying machine learning for data analysis. We have designed and assessed a prototype of a fraudulent transactions detection system that will be able to meet real-world demands and increase the security of transactions for the customers.

Facial Emotion Recognition (FER) is the approach of detecting emotions of humans from facial expressions. Emotions are detected automatically by the human brain and hence, many computer aided techn...

Surveillance is described as close observation; this term is used mostly when it comes to security observations and recording. Nowadays in our daily life we see the need of security rising up with the constant increase in the world of technology and features. On the other hand, the industries and firms continuously face threats and are pushed to a situation of seeking help for their safety. Also, that there are various blind spots in the regular security cam even the operator is not in a position to identify an emergency or any such need. This work is focused on providing clarity to such issues to the respective personal. It might be an industry, an office workspace, a supermarket or even a house. This concept deals with the enhancement of the surveillance system by infusing two different frames of Thermal and sRGB obtained input and give the output of noise reduced, enhanced and more visible image.

This Paper work proposes an Image cryptographic technique based on the Sudoku solution. The Sudoku solution can be reduced to a proper key clue by increasing the complexity level. The reduced clue for the Sudoku serves as the base key, which can be solved both at the Encryption and Decryption phase to reveal the Sudoku solution. Since a proper key clue leads to only one sudoku solution, the key is always stable. The Sudoku solution is expanded to the size of the Image to be encrypted using a sequence of expansion techniques. These techniques involve Number filling of the key matrix, Linear X-Y rotational shifting and Standard Zig zag pattern based reverse shifting. The derived cryptokey for an image dictates the details of positions to which the image pixels have to be shuffled. The shuffling is made at two levels namely Pixel level and Sub-Pixel (RGB) levels for an image, with the latter having more effective encryption. As this image encryption involves position shuffling in RGB layers, this technique falls under the Image scrambling method with partial diffusion. The Source and Encrypted images are analyzed for the crypto quality and thereby derive out the Performance metrics. The Distribution level of the source image pixels in the encrypted image is visually assessed by the absolute difference image. The Performance metrics reveal that the cryptography is more efficient, when the Sudoku is treated as a square matrix cryptokey and effectively used.

CoronaViruses (CoV) are significant human and creature pathogens. It is risky contamination that may cause Severe Acute Respiratory Syndrome (SARS-CoV), and Middle East Respiratory Syndrome (MERS-CoV). Clinical examinations have indicated that most COVID-19 patients experience the ill effects of lung disease. In spite of that, chest CT has been demonstrated to be a viable imaging strategy for lung-related illness analysis, chest X-ray is all the more generally accessible because of its quicker vision schedule and significantly less expensive CT. The main objective of this paper is to propose an efficient deep transfer learning algorithm for Covid-19 recognition in the chest using a 2D x-ray. DeepCOVID-Net to help radiology technicians consequently analyze COVID-19 in X-beams. To assess the model gassing, to gather hundred chest X-beam images of various victims affirmed by COVID-19 from GitHub repository and Kaggle. The dataset primarily consists of two classes: covid and normal. The proposed model (DeepCOVID-Net) is utilizing eight deep learning models that were chosen in this exploration for the training process. These are VGG19, DenseNet121, ResNetV2, InceptionV3, InceptionResNetV2, Xception, GoogleNet and MobileNetV2. Utilizing pre-trained deep transfer learning models shows effective performance metrics for each and every model. This paper explored the performance metrics of the proposed work with the existing neural network model.

Unmanned vehicles/systems technology is an emerging technology in recent years. Unmanned vehicles play a more significant role in many civil and military applications, such as remote sensing, surveillance, precision agriculture, and rescue operations rather than manned systems. The UAVs can gather photographs faster and more accurately than satellite imagery, allowing for more accurate assessment. This study provides a comprehensive overview of UAV civil applications, including classification and requirements. Also encompassed are research trends, critical civil challenges, and future insights on UAVs.

Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse weather and road conditions are some of the major reasons that contribute to 22% of accidents. People get injured and sometimes even lose their life due to road accidents that cause physical and mental instability. Regardless of the expertise of a good driver, at some point, an intelligent transportation system is necessary for the vehicle to make an immediate decision to avoid accidents. Road and weather-related mishap are those which occur due to adverse conditions like fog, winds, snow, rain, slick pavement, sleet, etc. Such accidents, though completely unavoidable, but can be reduced to some extent if proper measures are taken. Vehicle velocity, vehicle size, vehicle weight, momentum are a few of the reasons for a vehicle to go out of control. An intelligent accident avoidance system can predict the safe speed of a vehicle according to its size, weight, and momentum in different weather and road conditions. It can reduce the likelihood of accidents related to weather and road conditions. In this paper, we propose an Internet of Things (IoT) based intelligent accident avoidance system for adverse weather and road conditions. The proposed system comprises of an IoT system that perceives the environment for different weather and road conditions and a machine learning-based intelligent system that learns the adverse conditions that influence an accident to predict and suggest safe speed to the driver. The proposed system is experimented with real-time datasets and simulated using the Blynk application.

The main goal of HIPAA (Health Insurance Portability and Accountability Act) is to protect health information of individuals against access without consent or authorization. Security and privacy are the main issues in HIPAA. A compliant key management solution is used to reduce harm and risk while providing cryptographic mechanisms. Using ECC (Elliptic Curve Cryptography) we ensure more security for access of patient’s health records. This provides same level of security for access of patient’s health records. Patient’s health Information is stored in RFID cards. Finally, the proposed method ensures higher level of security than other existing cryptographic techniques. ECC provides more security even with small key sizes. Proposed scheme describes the various counter measures for improving security and a key recovery mechanism for the protection of keys.