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Sreelatha, S.,Dinesh, E.,Uma, C. Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.6
In recent years there has been a substantial increase in the use of functional foods for disease control. Fruits and vegetables produce phytochemicals such as flavonoids and antioxidants which can lower oxidative stress and reduce the risk of chronic ailments like cancer. The aim of the present study was to investigate the antioxidant capacity and the possible protective effects of Amaranthus paniculatus leaves on the antioxidant defense system in Ehrlich's ascites carcinoma (EAC)-treated mice. Oral administration of the leaf extract at different doses caused a significant decrease in tumor volume, viable cell count and tumor weight and elevated the life span of EAC bearing mice. It also showed an improved antioxidant potential as evidenced by a significant increase in the cellular antioxidant defense system such as catalase, superoxide dismutase and reduced glutathione and also significantly reduced the levels of TBARS. The levels of RBC, hemoglobin and lymphocyte count were altered in EAC bearing mice and were reverted back to near normal levels after the treatment with the leaf extracts. Their adequate content of total phenolics and flavonoids, DPPH scavenging activity which further suggests that the extracts exert a significant protection against oxidative stress conditions.
An Enhanced Text Mining Approach using Ensemble Algorithm for Detecting Cyber Bullying
Z.Sunitha Bai,Sreelatha Malempati International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5
Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.
An Ensemble Approach for Cyber Bullying Text messages and Images
Zarapala Sunitha Bai,Sreelatha Malempati International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.11
Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.
Stroke Disease Identification System by using Machine Learning Algorithm
K.Veena Kumari,K. Siva Kumar,M.Sreelatha International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.11
A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.