No abstract

No abstract

Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The idea system provides a key solution for this kind of problem that arises in the network and predicts the abnormal behavior of the sensor nodes as well. But built model by the proposed system various approaches in detecting these kinds of intrusions in any wireless sensor networks in the past few years. The proposed system methodology gives a phenomenon control over the wireless sensor network in detecting the inclusions in its early stages itself. The Data set pre-processing is done by a method of applying the minimum number of features for intrusion detection systems using a machine learning algorithm. The main scope of this article is to improve the prediction of intrusion in a wireless sensor network using AI- based algorithms. This also includes the finest feature selection methodologies to increase the performance of the built model using the selected classifier, which is the Bayes category algorithm. Performance accuracy in the prediction of different attacks in wireless sensor networks is attained at nearly 95.8% for six selected attributes, a Precision level of 0.958, and the receiver operating characteristics or the area under the curve is equal to 0.989.

Utilization in high-performance integrated circuits has been one of the most severe limitations in models in recent years.. Conditional discharge flip flop (CDFF) related to one of the earliest pulses caused flipflop reduces internal switching activities as that of existing explicit pulse triggered Data close to output flipflop (Ep-DCO). Registers are the main parts for processing information eg: in counters, accumulators etc.,. Implementation of these registers using CDFF can achieve low power consumption and high performance. MTCMOS (multi threshold CMOS) technique saves the leakage power during standby mode operations and hence, enhances the circuit performance for long battery life applications. We find that, using both MTCMOS and conditional discharge technique in flip flop, improves the performance and also consumes low power. In this paper, we simulate CDFF and the proposed MTCMOS CDFF to prove that MTCMOS CDFF is the best among the fastest pulse triggered flipflops. We also implement an application 4 bit shift register using proposed MTCMOS conditional discharge flip flop

The primary objective of steelmaking through Basic Oxygen Furnace (BOF) process is to achieve desired end point carbon content, temperature and percentage composition at the lowest cost and in the shortest possible time. As of now, most widely used models for prediction of parameters of converter steelmaking are mechanistic model, statistical model and neural network model for the prediction of the end point carbon content and temperature from BOF process parameters with reasonable accuracy. The (BOF) process is a widely preferred and effective steelmaking process due to its higher productivity and low production cost. The process of converter steel making is complicated and not completely understood as it involves multiphase physical chemical reaction at high temperature. Obtaining molten steel of desired chemical composition is the objective of the process. Obviously, in the converter steel making , the end point carbon content and temperature of the molten steel are important controlling parameters to ascertain whether the molten steel of desired quality is achieved or not.In the present paper, the authors have made an attempt to develop model for end point carbon and temperature with the latest methodology i.e., Adaptive eural Fuzzy Inference System (A FIS) and then have brought out the comparison of the results achieved in eural etwork and GR models. Results from A FIS model predict more accurately in contrast to those from BP model vis-à-vis the measured carbon content and temperature.