Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
Resume Screener System
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
This study is aimed to compare the spectral characteristics of bileaflet mechanical heart valve (MHV) sound signals produced by two different companies. For this, heart sound and ECG signals of patients whose aortic or mitral heart valves replaced using commonly used St. Jude and Sorin bileaflet MHV were recorded. Three features relating to frequency of heart sounds and three features relating to energy of heart sounds were obtained from power spectrum of the recorded heart sound signals. Then the features from same recording area and same heart valve were statically compared.
Data ingestion is fast and data life becomes very short. When a user searches a product in a retailer website, deriving recommendations at that point of time makes sense to the business to cross-sell other related products. The customer pulse is need to be understood, and IoT is a key driver to understand the market. Online retailers are seeking a solution for sudden data surge scalability protection during special events (planned /unplanned) like festive seasons to support a sudden increase in concurrent user base. With the large concurrent user base, Microservice based solution and application deployment are gaining an edge and soon it will become a defacto standard. With the increase of cloud usage (Platform, User, Service, etc.), industries are looking at solutions for more and more low cost, high scalable application deployment platform (PaaS) to make it a win-win deal with cloud providers. In this paper, the authors have proposed an agent-based architecture for auto scaling of an application deployed on the premises or in PaaS based on data surge sense at minimal cost without compromising on performance and other non-functional requirements in real time. To address this research issue, microservice based Twitter sentiment analysis application is created to ingest realtime data. Then this approach will define the guidelines to auto scale (Lorido-Botran et al., 2014) up/down the application based on incoming data surge to define dynamic and adaptive resource utilization, which in turn reduces infrastructure and service utilization cost of the cloud-based platform.
Social media has a high effect on our everyday lives. Peoples share their views, stories, news, and communicate events through internet based life. It leads to the huge shared information in the social media. It is not convenient to find and frame the essential events with the huge data, in most of the cases, browsing, searching, and monitoring events turns out to be very challenging. Major work has been done on topic detection and tracking (TDT) domain. Many of such methods are on the basis of single-modality (e.g., text, images) or multi-modality data. In the analysis of single-modality, various available methods acquire visual data (e.g., images and videos) or text based data (e.g., names, time references, locations, title, tags, and description) separately to design the event data for the event detection and tracking as well. This issue can be cleared by new multi-modal social event tracking and a transformative system for effectively capturing the events, as well as make the event synopsis in time. The authors present a novel method that works with the mmETM, which can viably make the social records, and it includes the extensive content incorporated with pictures. To coordinate the approach for social tracking, an incremental learning approach is obtained like mmETM that gives information and the event's visual topics in social media. To support this work, the authors have utilized an example informational index and regulated a few tests on it. Both the qualitative and the quantitative investigation on the proposed mmETM approach have shown some best in-class strategies.
Today's Indian society has transformed from a patriarchal and male dominated society to a broadminded one, where women work shoulder to shoulder with men. However, women have to manage their work life and family simultaneously. This ends in admitting their child to crèche throughout their job temporal order. The proposed system will help parents, especially working women to take care of their infants without being physically present. But when it comes to crèche or some outsider monitoring our babies, the level of care and concern can never be matched with that of parents. Hence, here we have a system that will facilitate the parents to monitor their babies without being physically present and assure utmost reliability. The demonstrated system gives the complete idea about how a baby monitoring system can be implemented using various sensors like temperature, humidity, PIR, and noise. Also, a DC Motor has been interfaced so that the cradle can swing automatically when the baby cries or its sleep is disturbed. The live status of the baby can be viewed on the webpage and the data from the sensors are dynamically updated. Also, notifications regarding baby's health status are sent to the parents through the mail as and when required. This system aims at assisting parents in taking satisfactory care of their babies.
Intrusion Detection Systems (IDS) have become a vital part of computer networks. Specific signatures of formerly identified attacks in a network and characterized traffic datasets are the two most significant parameters, which have been considered by various conventional IDS. Machine learning methods can be applied in IDS since they can learn from attacks' signatures or normal-operations occurring in the network. There is usually a large volume of data in intrusion detection systems in terms of both features and instances. But in this voluminous data, all features do not contribute to traffic thereby increasing the chances of false alarm generation. Therefore, efficiency and veracity of Intrusion Detection Systems can be reduced by selecting only a fair number of features. In this work, an IDS using a recursive feature selection algorithm has been proposed which aids to eliminate various irrelevant features and identify various relevant features of attacks in order to improve attack detection and reduce false alarm generation rate. The proposed IDS has also been analyzed and tested using a revised version of the KDD dataset in Scikit-learn library of Python.
