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Hepatocellular proliferation is one of the most common causes of hepatocellular carcinoma (HCC), a type of cancer that is widely distributed disease. Hepatocellular carcinoma treatment has numerous barriers, including ineffectiveness, side effects, and drug resistance to currently available treatments. Previous studies showed that a high intake of Brassica vegetables has been associated to a decreased risk of a number of malignancies. The aim of this study is the evaluation of antiproliferative activity of Brassica nigra seeds extract in mice exposed to phenobarbital. Brassica nigra seeds where extracted; phytochemical analysis of the extract was done that including phytochemical screening tests and Gas chromatography-mass spectrometry (GC-MS) analysis. Antiproliferative activity of hydro alcoholic Brassica seeds extract has been studied by 800mg/kg and compare with control group (given normal saline), phenobarbital group (Phenobarbital 75mg/kg) and combination group (Brassica extract 800mg/kg+ Phenobarbital 75mg/kg). The GC-MS analysis revealed the presence of isothiocynate compound. Histologically phenobarbital induced severe hepatocellular proliferation (hyperplasia and hypertrophy), glass ground cytoplasm, while Brassica seeds extract produce improvement in histopathological changes that include mild scattered proliferation picture and eosinophilic cytoplasm. In comparison to phenobarbital group, Combination groups pretreated with Brassica nigra seeds for 14 days and phenobarbital for 7 days caused significant reduction relative liver weight and alanine aminotransferase (ALT) Brassica nigra seeds extract have isothiocynate as main compound it showed antiproliferative action on the liver tissue, implying that it may have a promising effect in minimizing the risk of liver cancer.

The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A botnet is a collection of infected devices that are managed by a botmaster and communicate with each other via a command server in order to carry out malicious attacks. With the rise in malicious attacks, detecting botnet applications has become more challenging. Therefore, it is essential to investigate mobile botnet attacks to uncover the security issues in severe financial and ethical damages caused by a massive coordinated command server. Current state of the art, various solutions were provided for the detection of botnet applications, but in general, the researchers suffer various techniques of machine learning-based methods with static features which are usually ineffective when obfuscation techniques are used for the detection of botnet applications. In this paper, we propose an approach by exploring the concept of a deep learning-based method and present a well-defined Convolutional Neural Network (CNN) model. Using the visualization approach, we obtain the colored images through byte code files of applications and perform an experiment. For analysis of the results of an experiment, we differentiate the performance of the model from other existing research studies. Furthermore, our method outperforms with 94.34% accuracy, 92.9% of precision, and 92% of recall. [ABSTRACT FROM AUTHOR]

Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language. Lack of indexing, primarily semantic indexing of the daily news items, makes news items impracticable for any querying. Moreover, the most straightforward search facility does not support these unindexed news resources. Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages, i.e., Urdu. The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler. The corpus is then semantically linked and annotated with daily news items. Two techniques are proposed for image annotation, free annotation and fixed cross examination annotation. The second technique got higher accuracy. Build news ontology in protégé using OntologyWeb Language (OWL) language and indexed the annotations under it. The application is also built and linked with protégé so that the readers and journalists have an interface to query the news items directly. Similarly, news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves. [ABSTRACT FROM AUTHOR]

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India's total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers' reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS). [ABSTRACT FROM AUTHOR]