AES-Based Encoding and Decoding Images using MATLAB
A Novel Technique of Sign Language Recognition System using Machine Learning for Differently Abled Person
Implementation of Machine Learning Techniques for Depression in Text Messages: A Survey
A Study of Ransomware Attacks on Windows Platform
Techniques of Migration in Live Virtual Machine and its Challenges
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
Integrated Atlas Based Localisation Features in Lungs Images
Network and Cloud Data Centers generate a lot of data every second, this data can be collected as a time series data. A time series is a sequence taken at successive equally spaced points in time, that means at a particular time interval to a specific time, the values of specific data that was taken is known as a data of a time series. This time series data can be collected using system metrics like CPU, Memory, and Disk utilization. The TICK Stack is an acronym for a platform of open source tools built to make collection, storage, graphing, and alerting on time series data incredibly easy. As a data collector, the authors are using both Telegraf and Collectd, for storing and analyzing data and the time series database InfluxDB. For plotting and visualizing, they use Chronograf along with Grafana. Kapacitor is used for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the system admin.
Though Machine Learning is a discipline in software engineering, it contradicts from the usual computing styles. In usual computing style, algorithms are sets of expressly programmed instructions utilized by computers to compute or solve the problem. Machine learning algorithms rather take into consideration for computers to prepare on information sources and utilize measurable examination keeping in mind the end goal to yield esteems that fall inside a particular range. This paper addresses five most commonly used classification algorithms, such as Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Support Vector Machine. It aims to recover the incomplete sensed data of an IoT environment and proves that Linear Regression is the best suited for data recovery.
Cognitive Radio Networks (CRNs) are the key technology for the efficient utilization of spectrum in wireless networks in a dynamic manner. Mobile Ad hoc Networks (MANETs) using cognitive radio (CR) capability (CR-MANETs) has opened many areas of research. Among them routing in CR-MANETs is a challenging issue. Dynamic MANET On-Demand (DYMO) routing protocol is the successor of Ad hoc On-Demand Distance Vector (AODV) routing protocol. The drawback of AODV is that it does not support larger networks. It is resolved by DYMO routing protocol as it scales larger networks. But, DYMO does not support small network sizes with low mobility speeds. In this paper, CR-DYMO routing protocol for MANETs using cognitive capabilities is proposed using CogNS simulation framework to enhance the QoS performance for all network sizes. The simulations are evaluated using the various performance metrics, such as Average Throughput, Packet Delivery Ratio, Average End-to-End Delay, Routing overhead, Normalized Routing Load, Mean Jitter, Energy Consumed, and Energy Left through responsive (TCP) and non-responsive (UDP) traffic agents. From the results, it is observed that the Modified CR-DYMO routing protocol performs better when compared to DYMO and defacto CR-DYMO through the above-mentioned performance metrics under TCP and UDP traffic agents.
Automation is the mechanism to replace the human intervention in any process by the machine. Here the authors have considered the automation in the area of computer programming where researchers have tried to ease up the job of programmer by providing different tools and techniques to generate the programming code. The aim of this paper is to explore the research done in this area and give insight to the available automatic code generation methodologies for different types of input generating the code in different programming languages. Summary of all the available techniques has been presented in the paper.
Traditional teaching learning has transformed significantly towards offering a learner an experience that to a greater extent mimics a human tutor; while in a computer-based or valued learning environment, Machine Learning (ML) techniques implemented as algorithms have played a significant role. This paper is a review of different interventions of machine learning in selected types of teaching learning systems, presented as a descriptive analysis, recommendations emergent from this analysis have been presented. Further the possibility of applicability of these systems for supporting learning of individual with disabilities, has been explored and evidentially advocated machine learning algorithms hold tremendous potential in terms of enriching the systems, facilitating the learning of individuals with special needs by providing versatility and adoptive learning experiences learning effectiveness, and this thought has been further extended to a recommendation for individuals with a disability, essentially with the deemed design alternatives.