Volume 10 | Issue 12 |

 Abstract

Video in the national defense, education, monitoring, entertainment and other fields have been widely used, so data security on the internet cannot be ignored. Video encryption protects the original video information and improves the security of video information. Researchers have done a lot of research on video encryption and put forward a lot of video encryption methods. Video encryption methods are mainly divided into complete encryption and partial encryption algorithm. In order to improve the generalization performance of video encryption and reduce the amount of data in video encryption, this paper proposes a video encryption on regions of interest (ROI) method based on Faster R-CNN by combining machine learning with information security. The method trains a Faster R-CNN model using the ROI dataset firstly, and then uses the model to extract ROI in the video. Different encryption algorithms are used to encrypt ROI and non-ROI in the video respectively. To overcome the shortcomings of encryption algorithms that can only be used for a specific coded video, a special video encryption method is proposed to encrypt the video with different video coding structure and has better generalization performance. Compared with the encryption method in the video coding process, this method considers the content information of the video fully and has better performance. It can be concluded through experiments that the encryption method in this paper has the characteristics of higher security and less calculation.

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 Keywords

Video Encryption, Image Processing, Deep Learning

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Identification of faults via computer-based techniques is a growing trend in all fields these days. Two main characteristics of Bone Fracture Detection are fast identification and high precision which is described by highly sensitive device by incorporating advanced techniques and effective resource usage. The effect of undue external stress above the limits of what the bone may tolerate is a crack in a bone or bone fracture. Canny Edge detection is an image processing technique that identifies bone fracture by utilizing automatic fracture detection efficiently and overcomes the question of noise reduction. There are many methodologies accessible in today's world for edge detection, such as Sobel, Canny, Log, Prewitt, and Robert. These processes, though, are hampered by crucial limitations such as a lack of capacity to conduct multi resolution research, culminating in the failure to identify small information during the analysis. The other major drawback of the techniques is that they operate well with high resolution and high-quality pictures, but because of their intrinsic lack of ability to differentiate between edges and noise elements, they do not work well with blurry images. The approach being suggested uses the CNN algorithm to solve these issues. The findings of the simulations carried out suggest that the approach proposed is a far more effective system for conducting edge detection on aggregate scales. The suggested system has also shown to be sufficiently resilient to retrieve the required details and do the necessary analysis on key portions of the images and manage noise in a much better way than the edge detectors currently usable.

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 Keywords

Machine Learning, Image processing using X-ray images, Canny Edge Detection, SVM algorithm.

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Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this work, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved relevant studies from six electronic databases, of which we have selected five studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is machine learning in these models. After this observation based on the analysis of machine learning-based algorithm, to recognize machine learning, we conducted additional researches in databases on crop yields. To find studies that used machine learning, we also searched crop yield datasets. This further study reveals that the Decision Tree Method is the most frequently employed machine learning algorithm in these studies.

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 Keywords

Machine learning, Crop Yield Prediction, Decision tree algorithm

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 Abstract

Handwriting is one of the distinguishing characteristics that distinguishes a person's identity, and it is a method of identifying the writer's physical characteristics. It displays a person's genuine personality, including their actions, emotional outbursts, sense of self, rage, creativity, honesty, phobias, and a range of other traits. In this paper a multi-layered approach is proposed for analyzing personality traits by identifying the type of handwriting and classify the personality of the individual human being using deep learning models such as Resnet 34 and YOLO v5 model.

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 Keywords

Handwriting Analysis, Personality Classification, Feature, Deep Learning.

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Creative Commons Attribution 4.0 and The Open Definition

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This qualitative study investigated the lives of these, high-achieving students. It examined their experiences, characteristics, and behaviors. In this context, this illustrious study conceptualized. It is time to place the lives of high-achieving adolescents at the center of attention. The researcher strives to study the participants' real experiences through the lens of their narratives, disclosing and uncovering at every step. This study initiated a worthy effort to persuade policymakers to develop a support system for performing students.

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high-achieving students?experiences?characteristics?behaviors

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 Abstract

Medical field is more significant because it helps in the treatment and diagnosis of ailments, diseases and conditions and it break downs the molecular mechanism of every disease. The application of Different Equations (DE) in medical field has been executed in the prevailing work. DE refers to the mathematical statement that has involved one or more derivatives and it has also signified the rate of change of unceasing wavering quantities. DE has been general in science and engineering and in various fields of quantitative study, since it could be directly measured and perceived for systems which endured changes. Considering the medical field, DEs are used in Pathology, Cardiology, and dengue fever and in cancer therapy have been highlighted in the work. Moreover, challenges and the implementation of the DE has been explored in the work. Literature from 2018 -2022 has been preferred for the work. During the research, choosing the appropriate paper for the topics have been a broad task. The results shown that the data gained by the mathematical models could imbibe the Ordinary Differential Equation, Partial differential equations, Linear and Non-Linear equations, Homogeneous and Non-Homogeneous equations could be more precise than the data obtained from the classical model. The obtainable work will enact as a guide for future analysis based on the application of differential equation in Medical Field.

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 Keywords

Different Equations, Cardiology, Linear and Non-Linear equations, Pathology, Variable Order

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 Abstract

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 Keywords

Fluorescence, chemosensor, Schiff Base

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Herbal Cosmetic, Herbal Lipstick, Beetroot, Betanine. lips.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years, Artificial Intelligence (AI) has shown excellent progress and its potential is growing day by day. An application area of AI is Natural Language Processing (NLP). Voice assistants absorb AI by using cloud computing and can communicate with the users in natural language which we can actually use. Voice assistants are easy to use . There are millions of devices that incorporate them in households nowadays. The most common devices with voice assistants are smart speakers (Alexa, SIRI)and they have just started to be used in schools and universities. The purpose of this paper is to know how voice assistants and smart speakers are used in everyday life and the way a bot can help us in Hustle Free.

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Artificial Intelligence, Natural Language Processing, Smart Assistant

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 Abstract

Non-steroidal anti-inflammatory drugs (NSAIDs) are a class of drugs commonly used to treat both the acute and chronic injuries sustained by osteoarthritis, rheumatoid arthritis and ankylosing spondylitis athletes during training and competition. Aceclofenac is well tolerated and effective in reducing pain and/or improving functional capacity in chronic (OA, AS, RA) and acute (e.g. LBP) painful musculoskeletal conditions. It appears to be more effective than other NSAIDs and has a better Gl profile. The purpose of this research was to develop a matrix-type transdermal therapeutic system containing drug Aceclofenac with different ratios of Hydroxylpropyl methyl cellulose (HPMC) and Ethyl Cellulose and chitosan polymeric systems (F1-F9) by incorporating 25 % w/w of polyethylene glycol (plasticizer) and 0.1 ml DMSO (permeation enhancer). The physicochemical compatibility of the drug and the polymers studied by FTIR spectroscopy suggested absence of any incompatibility. Formulated transdermal films were physically evaluated with regard to thickness, weight variation, drug content, folding endurance, percentage of moisture content and percentage moisture uptake, tensile strength and In-vitro drug release studies of formulations were performed by using Franz diffusion cells.

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 Keywords

Transdermal drug delivery systems, skin , chitosan, transdermal patch

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Creative Commons Attribution 4.0 and The Open Definition