In this new era of cloud computing, Intrusion Detection System (IDS) is very essential for the continual monitoring of computing resources for signs of compromise since the number of attack vectors and malware are in increase. Only few IDS datasets are publicly available and those available are outdated, lack cloud‐specific attacks. This article presents a novel dataset based on Virtual Machine Introspected data for the implementation of IDS in cloud. The dataset was generated from the behavioral characteristics of malware and benign sample execution traces on virtual machines using Virtual Machine Introspection (VMI) technique. A vector space model based on system call approach is applied to analyze the behavioral characteristics for the generation of proposed dataset. The purpose of this study is to compare the proposed dataset with existing datasets and evaluate the effectiveness of these datasets by applying Machine Learning (ML) algorithms with 10‐fold cross‐validation. The ML algorithms used in the experiments are C4.5, Random Forest, JRip, NaiveBayes, K‐Nearest Neighbors (KNN), and Support Vector Machine (SVM). The effectiveness of detecting intrusions using proposed dataset is promising compared with other datasets in‐terms of intrusion detection accuracy, recall value, precision, and F1‐score metrics. For example, the intrusion detection accuracy in proposed dataset is 0.11% improved than UNM dataset, 6.28% higher than ADFA dataset, and 1.88% higher than LID dataset with C4.5 algorithm. Therefore, the proposed dataset is best suitable for implementing IDS for cloud.

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To minimize long-term structural damage and irreversible visual loss, glaucoma must be diagnosed early. Glaucoma is diagnosed by assessing visual activity as well as monitoring structural damage to the optic nerve. Transparent laser ophthalmoscopy scanning, and laser polarimetry are examples of computer-based equipment that can aid in the analysis of visual acuity and structure. Most persons with glaucoma do not experience any early signs or pain. Glaucoma must be diagnosed and treated by an eye doctor on a regular basis. Traditional glaucoma detection procedures include an eye doctor analyzing photographs and looking for irregularities. Because the image contains noise and other elements that make appropriate analysis difficult, this method takes a long time and is not always precise. In addition, a machine that has been educated for analysis becomes more accurate than a human analyst. The proposed method will automate this procedure by employing deep learning techniques in image processing to diagnose glaucoma without the need for human intervention.