Performance evaluation of botnet detection using machine learning techniques

Cybersecurity is seriously threatened by Botnets, which are controlled networks of compromised computers. The evolving techniques used by botnet operators make it difficult for traditional methods of botnet identification to stay up. Machine learning has become increasingly effective in recent years as a means of identifying and reducing these hazards. The CTU-13 dataset, a frequently used dataset in the field of cybersecurity, is used in this study to offer a machine learning-based method for botnet detection. The suggested methodology makes use of the CTU-13, which is made up of actual network traffic data that was recorded in a network environment that had been attacked by a botnet. The dataset is used to train a variety of machine learning algorithms to categorize network traffic as botnet-related/benign, including decision tree, regression model, naïve Bayes, and neural network model. We employ a number of criteria, such as accuracy, precision, and sensitivity, to measure how well each model performs in categorizing both known and unidentified botnet traffic patterns. Results from experiments show how well the machine learning based approach detects botnet with accuracy. It is potential for use in actual world is demonstrated by the suggested system’s high detection rates and low false positive rates

Analysis of Fats and Fat-Soluble Components in Foods Using Supercritical Fluid Extraction and Chromatographic Techniques

The purpose of this study was to develop experimental procedures for the extraction of fats from common foods using SFE and to determine by chromatographic techniques the fat-soluble components that are extracted with the fats. In this study various food samples were examined including french fries from Wendy\u27s, McDonald\u27s and Burger King, as well as potato chips, peanut butter and donuts. Soxtec extraction was used as the standard method for comparison of results (percentage of fat) obtained from SFE. Gas chromatography and gas chromatography/mass spectrometry were used for the determination of fat-soluble components (fatty acids), after extraction of fat from food products

Erdheim-Chester disease with multisystemic involvement: a diagnostic challenge

Erdheim–Chester disease (ECD) is a rare, non-inherited, non- Langerhans form of histiocytosis of unknown origin, first described in 1930. This entity is defined by a mononuclear infiltrate consisting of lipid laden, foamy histiocytes that stain positively for CD68. Individuals affected by this disease are typically adults between their 4th and 6th decades of life. The multi systemic form of ECD is associated with significant morbidity, which may arise due to histiocytic infiltration of critical organ systems. Among the more common sites of involvement are the skeleton, central nervous system, cardiovascular system, lungs, kidneys (retroperitoneum) and skin. The most common presenting symptom of ECD is bone pain. Bilateral symmetric increased tracer uptake on 99mTc bone scintigraphy affecting the periarticular regions of the long bones is highly suggestive of ECD. However, definite diagnosis of ECD is established only once CD68(+), CD1a(−) histiocytes are identified within a biopsy specimen with aid of clinical and radiological data. Here we present a rare case of Erdheim-Chester disease in a 46 year male patient based on clinical data, radiological data, histopathological and immunohistochemistry findings

Academic integrity: looking beyond plagiarism

Plagiarism is a growing concern for academia across the globe. Several factors influence the behaviour of the researcher towards plagiarism. The UGC (Promotion of Academic Integrity and Prevention of Plagiarism in HEIs) Regulation, 2018 was notified to promote academic integrity in HEIs and curb plagiarism. However, this regulation has many gaps which need to be addressed in the quest for achieving academic integrity. This paper is an attempt to identify these gaps in the regulation. It also attempts to address the over reliance of academic fraternity on Plagiarism Detection Tools

Comparison of oral nifedipine and oral labetalol as a single drug therapy for control of blood pressure in preeclampsia

Background: Worldwide hypertension during pregnancy is a common cause of maternal and fetal morbidity and mortality. Effective control of blood pressure is one of the important steps in management of preeclampsia. Few drugs like nifedipine, labetalol, methyldopa, and hydralazine have acceptable high safety profile during pregnancy.Methods: In this study 120 antenatal women with non-severe preeclampsia were compared by giving either nifedipine or labetalol as a single drug therapy for control of blood pressure. Various parameters like control of blood pressure, side effects of drugs, gestational age at the time of delivery, mode of delivery, any complication and perinatal outcome were assessed.Results: In this study authors found that in both group, adequate control of blood pressure was achieved. This study shows slightly higher rate of pre term delivery and LSCS with labetalol and minimal side effects with nifedipine but difference in each group is insignificant.Conclusions: Labetalol and nifedipine both the drugs are equally effective in reducing blood pressure and any of it can safely be used as a first choice of drug for management of hypertension in preeclampsia and it can be decided as per clinician’s experience and familiarity with drug

Academic integrity: looking beyond plagiarism

190-197Plagiarism is a growing concern for academia across the globe. Several factors influence the behaviour of the researcher towards plagiarism. The UGC (Promotion of Academic Integrity and Prevention of Plagiarism in HEIs) Regulation, 2018 was notified to promote academic integrity in HEIs and curb plagiarism. However, this regulation has many gaps which need to be addressed in the quest for achieving academic integrity. This paper is an attempt to identify these gaps in the regulation. It also attempts to address the over reliance of academic fraternity on Plagiarism Detection Tools

SEM-EDAX Analysis of Jarita Vanga and Vanga Bhasma

Use of Vanga Bhasma can traced back from ancient era. Detailed description regarding the procedure is available in various Rasagranthas. Jarana is a special technique mentioned in recent Rasa treatises for Puti Lohas which is an intermediate stage between Shodhana and Marana. In this study, after performing both Samanya and Vishesha Shodhana, Vanga was subjected to Jarana using Ashwatha Twak as per the reference of Rasa Tarangini followed by Prakshalana to remove its alkaline nature. Later Vanga was subjected to Putapaka using Bhavana Dravya as Kumari Swarasa. Organoleptic and Elemental constitutions of both Jarita Vanga and Vanga Bhasma where analysed to see the differences and to observe the changes due to Samanya Shodhana and Vishesha Shodhana

Review article on Swarna Parpati with special reference to Aushadhi Gunadharma Shastra

In Ayurveda, Swarna (gold) Bhasma in different formulations has been administered to patients as a therapeutic agent for several clinical disorders including respiratory disorders, rheumatoid arthritis, diabetes mellitus and nervous system diseases. It is one of the metals which is even indicated since the birth. Parapati Kalpana is well known and successfully used preparations for the management of Grahani Roga. Swarna Parpati is one of the formulation of Ayurveda which comes under Parpati Kalpana. This article has reviewed Swarna Parpati from different classics with special reference to book Aushadhi Gundharma Shastra of Acharya Gune Shastri

Review on unexplored Asava Arishthas of Gada Nighraha

Gada Nigraha is very well known ancient text of Ayurveda. It acquaints us with many indispensable formulations. Many of these formulations are unfathomed. Eminently many Asava Arishtas mentioned by Acharya are very unique in their method of preparation like Gugguluvasava, Tambulasava, Kushmandasava, Gandikadronasava, Narikelasava etc. here an effort is made to accentuate these formulations with intention of carrying further exploration in this regard

Automatic Kidney Stone Detection Using Deep learning Method

Kidney stone disease is a common urological illness that affects millions of people worldwide. The identification of kidney stones early and accurately is critical for timely intervention and effective management of this illness. Deep learning approaches have showed promising results in a variety of medical image processing jobs in recent years. This paper describes a novel deep learning-based approach for automatic kidney stone diagnosis utilising medical imaging data. A convolutional neural network (CNN) architecture is used in the suggested method to identify and classify kidney stones in medical photographs. A huge collection of kidney stone images is first collected and preprocessed to ensure homogeneity and improve feature extraction capabilities. To optimise its performance, the CNN model is trained on this dataset using a large number of annotated samples. The trained CNN model distinguishes kidney stone presence from healthy regions in medical pictures with good accuracy and robustness. It detects kidney stones of various sizes and shapes while overcoming hurdles given by different stone compositions and human anatomy. Furthermore, the deep learning model has fast processing speeds, making it suited for real-time clinical applications. Extensive validation and testing on an independent dataset are performed to evaluate the model's performance. The results show that the proposed deep learning method is effective in autonomous kidney stone identification, with sensitivity, specificity, and accuracy metrics comparable to or exceeding those of existing classical methods