Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility of the proposed IDS system by several experiments on NSL-KDD dataset. A reduction process has been used to reduce the number of features in order to decrease the complexity of the system. The experimental results show that the proposed system is able to speed up the process of intrusion detection and to minimize the memory space and CPU time cost.

Recently, intensifying energy crises accompanying ecological crises due to the decline in fossil-fuel reserves and extensive greenhouse gas emissions have triggered the exploration of renewable substitutes for petro-diesel. In this scenario, biodiesel is the best alternative to non-renewable finite conventional fuels due to its cost-effectiveness, sustainability, renewability, biodegradability, and eco-friendly nature. Hence, the current research was designed to utilize scanning electron microscopy to investigate and identify the micro-morphological characteristics of selected seed-bearing crops. Light-microscopy (LM) indicated discrete variations in macro-morphological characters such as seed shape (ovoid, ovate, oblong, semi-spheroid, or discoid), seed size (3.5–14 mm in length and 2.25 to 6.5 mm in width), seed color (yellow to black), and number of seeds per kilogram (from 6000 to 260,000). Chemical extraction via Soxhlet apparatus resulted in the estimation of oil content within the range of 20.3–48.0% (wt./wt.), FFA content (0.63–6.91 mg KOH/g), and maximum product, i.e., 98% biodiesel yield was achieved. Multivariate analysis via principal component analysis (PCA) was done using PAST 3 software to investigate similarities and differences among factors/variables. SEM examination exhibited ultra-morphological characters and distinct variation in cell-wall ornamentation; hilum occurrence, position, and level; wall-sculpturing variations such as ruminate, verrucate, wrinkled, or striate; cell arrangement (anticlinal or periclinal); and cell shape and margins. To conclude, SEM could be an advanced technique to disclose the ultra-micromorphological characteristics of oil-bearing energy crops providing a convenient way for scientists to determine correct identification, authentication, and classification.