An efficient hybrid system for anomaly detection in social networks

Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s)

Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach

Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. © 2013 IEEE

Mechanistic insight of Staphylococcus aureus associated skin cancer in humans by Santalum album derived phytochemicals: an extensive computational and experimental approaches

An excessive amount of multidrug-resistant Staphylococcus aureus is commonly associated with actinic keratosis (AK) and squamous cell carcinoma (SCC) by secreted virulence products that induced the chronic inflammation leading to skin cancer which is regulated by staphylococcal accessory regulator (SarA). It is worth noting that there is currently no existing published study that reports on the inhibitory activity of phytochemicals derived from Santalum album on the SarA protein through in silico approach. Therefore, our study has been designed to find the potential inhibitors of S. aureus SarA protein from S. album-derived phytochemicals. The molecular docking study was performed targeting the SarA protein of S. aureus, and CID:5280441, CID:162350, and CID: 5281675 compounds showed the highest binding energy with −9.4 kcal/mol, −9.0 kcal/mol, and −8.6 kcal/mol respectively. Further, molecular dynamics simulation revealed that the docked complexes were relatively stable during the 100 ns simulation period whereas the MMPBSA binding free energy proposed that the ligands were sustained with their binding site. All three complexes were found to be similar in distribution with the apoprotein through PCA analysis indicating conformational stability throughout the MD simulation. Moreover, all three compounds’ ADMET profiles revealed positive results, and the AMES test did not show any toxicity whereas the pharmacophore study also indicates a closer match between the pharmacophore model and the compounds. After comprehensive in silico studies we evolved three best compounds, namely, Vitexin, Isovitexin, and Orientin, which were conducted in vitro assay for further confirmation of their inhibitory activity and results exhibited all of these compounds showed strong inhibitory activity against S. aureus. The overall result suggests that these compounds could be used as a natural lead to inhibit the pathogenesis of S. aureus and antibiotic therapy for S. aureus-associated skin cancer in humans as well

Evaluation of Adenanthera pavonina-derived compounds against diabetes mellitus: insight into the phytochemical analysis and in silico assays

Adenanthera pavonina is a medicinal plant with numerous potential secondary metabolites showing a significant level of antidiabetic activity. The objective of the current study was to identify potential phytochemicals from the methanolic leaf extract of Adenanthera pavonina as therapeutic agents against diabetes mellitus using GC-MS and in silico methods. The GC-MS analysis of the leaf extract revealed a total of 17 phytochemicals. Molecular docking was performed using these phytochemicals, targeting the mutated insulin receptor tyrosine kinase (5hhw), which inhibits glucose uptake by cells. Diazoprogesterone (−9.2 kcal/mol), 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid (−6.9 kcal/mol), and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] (−6.6 kcal/mol) exhibited better binding with the target protein. The ADMET analysis was performed for the top three compounds with the best docking scores, which showed positive results with no observed toxicity in the AMES test. Furthermore, the molecular dynamics study confirmed the favorable binding of Diazoprogesterone, 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] with the receptor throughout the 100 ns simulation period

MRI-based Brain Tumor Image Classification Using CNN

Though all brain tumors are not cancerous but they caused a critical disease produced by irrepressible and unusual dividing of cells. For the case of Medical diagnostics of many diseases, the health industry needs help, the current development in the arena of deep learning has assisted to detect diseases. In recent years medical image classification has gained remarkable attention. The most well-known neural network model for image classification problems is the Convolutional Neural Network (CNN). CNN is the frequently employed machine-learning algorithm that is used in Visual learning and Image Recognition research. It is considered to derive features adaptively through convolution, activation, pooling, and fully connected layers. In our paper, we present the convolutional neural network method to determine cancerous and non-cancerous brain tumors. We also used Data Augmentation and Image Processing to classify brain (Magnetic Resonance Imaging (MRI). We used two significant steps in our proposed system. First, different image processing techniques are used to preprocess the images and secondly we classify the preprocessed image using CNN. Brain tumor classification is a process of identifying and separating the cancerous and non-cancerous brain tissues and labeling them automatically. We use the famous machine learning algorithms Convolutional Neural Network which is broadly employed for image classifications. This experiment is conducted on a dataset of 2065 images. In our dataset number of training, examples are 1445, the number of validation examples is 310, and the number of testing example is 310. We also used data augmentation to raise the number of the dataset. We achieved a high testing accuracy of 94.39%. The proposed system displayed sufficient accuracy on the dataset and beat many of the noticeable present methods

Regulation of the neural niche by the soluble molecule Akhirin

Though the adult central nervous system has been considered a comparatively static tissue with little turnover, it is well established today that new neural cells are generated throughout life. Neural stem/progenitor cells (NS/PCs) can self‐renew and generate all types of neural cells. The proliferation of NS/PCs, and differentiation and fate determination of PCs are regulated by extrinsic factors such as growth factors, neurotrophins, and morphogens. Although several extrinsic factors that influence neurogenesis have already been reported, little is known about the role of soluble molecules in neural niche regulation. In this review, we will introduce the soluble molecule Akhirin and discuss its role in the eye and spinal cord during development

CRICRATE : A cricket match conduction and player evaluation framework

Cricket has appeared as one of the most favorite outdoor games in the present world. The cricket players represent a country and create economic, political, and diplomatic relations among nations. The cricket board of a country requires selecting the fittest players for the upcoming team among some good players. We propose an architecture called Cricket Match Conduction and Player Evaluation Framework by developing some algorithms to predict the score of the players as well as the algorithm to evaluate the man of the match in one day or test cricket match. We implemented the framework by Weka and web technology. © Springer Nature Singapore Pte Ltd. 2019

Regulation of the neural niche by the soluble molecule Akhirin

Though the adult central nervous system has been considered a comparatively static tissue with little turnover, it is well established today that new neural cells are generated throughout life. Neural stem/progenitor cells (NS/PCs) can self‐renew and generate all types of neural cells. The proliferation of NS/PCs, and differentiation and fate determination of PCs are regulated by extrinsic factors such as growth factors, neurotrophins, and morphogens. Although several extrinsic factors that influence neurogenesis have already been reported, little is known about the role of soluble molecules in neural niche regulation. In this review, we will introduce the soluble molecule Akhirin and discuss its role in the eye and spinal cord during development

Cyberbullying detection on social networks using machine learning approaches

The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE

Tsukushi expression is dependent on Notch signaling and oscillated in the presomitic mesoderm during chick somitogenesis

During somitogenesis, segmentation of the body axis occurs by epithelial somites budding off from the rostral end of the unsegmented presomitic mesoderm (PSM), and its molecular regulation is achieved by a molecular oscillator and signaling molecules. Tsukushi (TSK) is a unique secreted protein and involved in diverse biological cascades in vertebrate embryos by modulating several signaling pathways at the extracellular region. However, the involvement of TSK in somitogenesis remains unknown. In this study, we investigated the detailed expression patterns of TSK at different developmental stages of a chick embryo. Chick-TSK (C-TSK) is expressed in the PSM and shows an oscillation pattern with three phases. The oscillation pattern of C-TSK in the PSM is similar to that of c-Notch1 and c-haity1, but not to c-Delta1. Our in vitro data showed that Notch signaling is necessary for the normal expression of C-TSK and that expression of C-TSK is an intrinsic property of the anterior PSM. These data suggest that TSK plays a role in chick somitogenesis. (C) 2015 Elsevier Inc. All rights reserved