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Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of traffic safety control policy. This paper deals with the some of classification models to predict the severity of injury that occurred during traffic accidents. I have compared Naive Bayes Bayesian classifier, AdaBoostM1 Meta classifier, PART Rule classifier, J48 Decision Tree classifier and Random Forest Tree classifier for classifying the type of injury severity of various traffic accidents. The final result shows that the Random Forest outperforms than other four algorithms.

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Summary As Network traffic rises and attacks become more widespread and complicated, we must come across Innovative ways to enrich Intrusion Detection Systems in Cloud Computing. This paper proposes the Ensemble approaches for Network Intrusion Detection and Classification in Cloud. The major aids of the Ensemble Learning to improve the outcome of each Machine Learning Algorithms and to get a robust Classifier. Real Time Malicious Network Streams Samples were collected using Honeynet, which is deployed on cloud environment. We use supervised learning and Unsupervised learning algorithms for classifying the known malicious network streams and unknown malicious streams. Network related attacks can be segregated into four classes, namely, Denial of service (DOS), User to root (U2 R), Remote to local (R2L), and probe, and the vital constraints that must be overcome with the end goal to build efficient Intelligent Intrusion Detection. The motivation behind the proposed work is to enhance the accuracy rate with response time. The outcome obtained from the Ensemble method has better accuracy rate compared to the SVM, Naive Bayes, and Logistic regression method.

Cloud computing security is the most critical factor for providers, cloud users, and organizations. The various novel approaches apply host‐based or network‐based methods to increase cloud security performance and detection rate. However, due to the virtual and distributed environment of the cloud, conventional network intrusion detection systems (NIDS) have been unreliable in handling these security attacks. Therefore, we design a methodology that incorporates feature selection and classification using ensemble techniques to provide efficient and accurate intrusion detection to address these problems. This proposed model combines the three most effective feature selection techniques (gain‐ratio, chi‐squared, and information gain) to offer a qualifying result and four top classifiers (SVM, LR, NB, and DT) using enhanced weighted majority voting. Moreover, we proposed an experimental technique using a new dataset called Honeypot. All experiments utilized three datasets: Honeypots, Kyoto, and NSL: KDD. In addition, the results of this experimental study were compared with other approaches and performed the statistical significance analysis. Finally, the results reveal that the proposed intrusion detection based on the Honeypot dataset was better and more efficient than other methods because we have an accuracy of 98.29%, FAR of 0.012%, DR of 97.9%, and AUC = 0.9921.

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Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

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