Volume 9 | Issue 6 |

 Abstract

Malicious URL, a.k.a. malicious site, is a typical and genuine danger to network safety. Vindictive URLs have spontaneous substance (spam, phishing, drive-by downloads, and so on) and bait clueless clients to become casualties of tricks (financial misfortune, burglary of private data, and malware establishment), and cause misfortunes of billions of dollars consistently. It is basic to identify and follow up on such dangers in a convenient way. Customarily, this recognition is done for the most part through the utilization of boycotts. Be that as it may, boycotts can't be thorough, and need the capacity to distinguish recently created malignant URLs. To improve the over-simplification of noxious URL locators, ML methods have been investigated with expanding consideration as of late. This article points to give a thorough review and an underlying comprehension of Malicious URL Detection strategies utilizing Ml. We present the proper detailing of Malicious URL Detection as an AI task, and arrange and audit the commitments of writing contemplates that tends to various measurements of this issue (highlight portrayal, calculation plan, and so forth) Further, this article gives an opportune and exhaustive overview for a scope of various crowds, not just for ML specialists and engineers in scholarly community, yet in addition for experts and professionals in network safety industry, to help them comprehend the cutting edge and work with their own exploration and useful applications. We likewise examine functional issues in framework configuration, open exploration difficulties, and point out significant bearings for future research.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Malicious, Legitimate, Machine Learning, Online Learning, Internet security, Cybersecurity,URL

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The article discusses a generalization of model of economic growth with uniform pace, which takes into account the effects of dynamic memory. Memory means that endogenous or exogenous variable at a given time depends not only on their value at that time, but also on their values at previous times. To describe the dynamic memory we use derivatives of non-integer orders. We obtain the solutions of fractional differential equations with derivatives of non-integral order, which describe the dynamics of the output caused by the changes of the net investments and effects of power-law fading memory.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

economic growth model, memory effects, dynamic memory, fading memory, derivative of non-integer order, fractional derivative, economic processes with memory

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

47H10, 54H25, 54C60, 46B40.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The aim of present work was to prepare formulations of poyherbal handwash from the metholonic extracts of leaves of Tridax procumbens, Azadirachta indica and lemon juice. Two formulations of hand wash were prepared and the formulations were evaluated for physical properties like appearance, pH and viscosity. The antimicrobial activity of prepared formulations of hand wash was checked against skin pathogens Bacilus subtilus, Staphylococcus aureus, Psuedomonas aeruginosa and Escherichia coli by agar diffusion method. The results revealed that prepared herbal hand wash formulations showed significant zone of inhibition compared with standard antibiotic drug (Amoxicillin). So these plant materials can be used in the preparation of herbal hand wash on commercially scale.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ployherbal handwash, Antimicrobial activity, Tridax procumbens and Azadirachta indica.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Rapid and accurate identification of plant diseases is essential for sustainable increases in agricultural productivity. Human experts have traditionally been relied upon to diagnose diseases, pests, nutritional shortages, and severe weather abnormalities in plants. This however is costly, time-consuming, and not practicable in some situations. The study of the use of pictorial methods for plant recognition has become a hot topic to address these challenges. We review the recent studies in the field of identifying pesticides and diseases utilizing imaging and machine learning in this paper. We expect this work to serve as a valuable resource for researchers who use image processing techniques to recognize crop pests and disease. In particular, we concentrate on the use of RGB images due to the low cost and high accessibility of RGB cameras. Deep learning instead of superficial classifications using manufactured characteristics has been at the forefront of recent efforts. The accuracy of the recognition on a specific dataset has been recorded by researchers; in some cases, the performance of these systems has deteriorated significantly when assessed on different datasets or under field conditions. However, it was promising to make progress to date. The experimental findings are present in ten CNN leaf disease recognition architectures, showing the accuracy, memory, precisely, specification, F1 score, training duration, and storage specifications. Recommendations are subsequently provided on the most appropriate architectures to be used in both traditional and mobile computing environments. We also explore some outstanding issues to be tackled to establish realistic systems for recognizing automatic plant diseases in field conditions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Plant disease detection; Classification; Machine Learning, Convolutional Neural Network.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Buying, Selling, Renting Bike Part, Online Order, Registration

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Creative Commons Attribution 4.0 and The Open Definition

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In todays world of technology human cannot survive without be- ing techno-freak. Just to get workplace environment friendly we are going to introduce six emotions and positive and negative emotion recognition methods using facial image and the the development of app based on the method. In this project we will use the Deep Learning technology to generate models with emotion based facial expressions to recognized emotions. Inevitebly feelings play an important role not only in our relations with other people but also in the way we use Computers. Affective computing is a domain that focuses on user emotions while he inter- acts with computers and applications. As emotional state of person may influence concentration, task solving and decision making skills, effective computing vision is to make system stable to recognize and influence human emotions in order to enhance productivity and ef- fectiveness of working with computers. We will develop an automated system to recognize six emotions along with positive and negatives in graphs and percentages. Thus, we recognize six emotions such as Angry, Disgust, Fear, Happy, Sad, Surprise. Also classified the calculated emotion recognition scores into pos- itive, negative and neutral emotions. Then we will implement an app that provides the user with six emotions scored and positive and negative emotions

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image Processing, CNN, LPBH(Local Binary Pattern Histogram), AWS cloud, S3 Bucket, Haar Cascades, Feature Extractions, deep learning, Facial Images.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Vegetables contain lots of beneficial phytochemicals and also have antioxidant and antimicrobial properties. Phytochemicals are chemical compounds which are produced by plants, generally help in resistance of fungi, bacteria and plant virus infection. Various phytochemicals found in vegetables are tannins, cardiac glycosides, terpenoids, saponins, phytosterols, alkaloids, flavanoids. Antimicrobial properties of vegetables have been found to be very effective against Escherichia coli, Streptococcus pyogenes, Klebsiella peumoniae, Pseudomonas aeroginosa, Bacillus subtilis which have been examined by disc- diffusion method. This review includes the study on Bottle gourd(Lagenaria siceraria), cucumber( Cucumis sativas), pumpkin( Cucurbita), ridged gourd( Luffa), karella ( Momordica charantia), tinda ( Praecitrullus fistulosus). Vegetables also have therapeutic properties like anti- diabetic, anti- ulcers, anti- inflammatory, anti- oxidant, anti- tumor properties.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Phytochemicals, antimicrobial activity, antioxidant, flavonoids, alkaloids

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Creative Commons Attribution 4.0 and The Open Definition