An Intrusion Detection System (IDS) is a software application or device that monitors the system or activities of network for policy violations or malicious activities and generates reports to the management system. A number of systems may try to prevent an intrusion attempt but this is neither required nor expected of a monitoring system. The main focus of Intrusion detection and prevention systems (IDPS) is to identify the possible incidents, logging information about them and in report attempts. In addition, organizations use IDPS for other purposes, like identifying problems with security policies, deterring individuals and documenting existing threats from infringing security policies. IDPS have become an essential addition to the security infrastructure of nearly every organization. Various methods can be used to detect intrusions but each one is specific to a specific method. The main goal of an intrusion detection system is to detect the attacks efficiently. Furthermore, it is equally important to detect attacks at a beginning stage in order to reduce their impacts. This research work proposed a new approach called outlier detection where, the anomaly dataset is measured by the Neighborhood Outlier Factor (NOF). Here, trained model consists of big datasets with distributed storage environment for improving the performance of Intrusion Detection system. The experimental results proved that the proposed approach identifies the anomalies very effectively than any other approaches

RFID is a technology similar to that of bar code scanning. An RFID system consists of tags, which use radio frequency signals to transmit its location information to a reader, which usually sends this information to a server that processes it according to the needs of the application. This paper presents a system that can track buses across a city by placing RFID tags in the buses and the readers in every alternative bus stop. The local server for the city recieves the location information, and alerts the forthcoming bus stops in the route of the bus, of the bus' number, route and expected time of arrival, which are then displayed at the stop. This system thus describes is a cost effective and easy to implement scheme for tracking buses in real time.

This work exhibits an efficient women security recognition system utilizing GPS and GSM modems. Inspite of different enormous growth in technology, science and mechanical engineering today world creates so many threats and risky spot for woman and for some kids. Women are living in the basic environment and society in which the dread of wrongdoing is dependably in high system. Today, viciousness against women unavoidably expanded. This paper targets a gadget which guarantees the assurance of women. This gadget enables women in the risky circumstances by recognizing their area, to secure and call the closest assets. Whenever you sense risk, you should simply, hang on the catch of the gadget. The system comprises of an ARDUINO NANO Controller, Bluetooth module, Taser and an Android versatile. When it is actuated, it tracks the spot of the women utilizing GPS (Global Positioning System) and sends crisis messages utilizing GSM (Global System for Mobile correspondence) through Android portable Application to SOS contacts (Predefined Numbers). In the event that the message isn't checked by the specific number referenced, the system will give the call to the predefined number. It is the convenient gadget for the lady which will probably convey and simple to utilize. This gadget is joined to Taser will create a stun which will assault the abusers. The utilization of advanced parts guarantees precision and makes it solid. The system additionally gives voice acknowledgment includes in the Android versatile Application which will help in sort of crisis circumstances when the gadget is harmed.

The chance of developing "Alzheimer's Disease (AD)" increases every 5 years after 65 years of age, making it a particularly common form of neurodegenerative disorder among the older population. The use of "Magnetic Resonance Imaging (MRI)" to diagnose AD has grown in popularity in recent years. A further benefit of MRI is that it provides excellent contrast and exquisite structural detail. As a result, some studies have used biological markers backed by "structural MRI (sMRI)" to separate AD populations, which indicate differences in brain tissue size and degradation of the nervous system. The lack of properly segmented regions and essential features by the existing models might affect classification accuracy for AD. The categorization of AD in this study is based on sMRI. In this research, the hybrid Deep-Learning Models "SegNet and ResNet (SegResNet)" have been proposed for segmentation, feature extraction, and to classify the AD. SegNet network is used to identify and segment specific brain areas. Edges and circles are the SegNet's first levels, whereas the deeper layers acquire more nuanced and useful features. SegNet's last deconvolution layer produces a wide range of segmented images linked to the 3 categorization labels "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)", and "AD" which the machine has earlier found out. To increase classification performance, the attributes of each segmented sMRI image serve as strong features of the labels. To enhance the feature information used for classification, a feature vector is built by combining the values of the pixel intensity of the segmented sMRI images. ResNet-101 classifiers are then used for characterizing vectors to identify the presence or absence of AD or MCI in each sMRI image. In terms of detection and classification accuracy, the proposed SegResNet Model is superior to the existing KNN, EFKNN, AANFIS, and ACS approaches