Machine learning in 3D space gesture recognition

The rapid increase in the development of robotic systems in a controlled and uncontrolled environment leads to the development of a more natural interaction system. One such interaction is gesture recognition. The proposed paper is a simple approach towards gesture recognition technology where the hand movement in a 3-dimensional space is utilized to write the English alphabets and get the corresponding output in the screen or a display device. In order to perform the experiment, an MPU-6050 accelerometer, a microcontroller and a Bluetooth for wireless connection are used as the hardware components of the system. For each of the letters of the alphabets, the data instances are recorded in its raw form. 20 instances for each letter are recorded and it is then standardized using interpolation. The standardized data is fed as inputs to an SVM (Support Vector Machine) classifier to create a model. The created model is used for classification of future data instances at real time. Our method achieves a correct classification accuracy of 98.94% for the English alphabets’ hand gesture recognition. The primary objective of our approach is the development of a low-cost, low power and easily trained supervised gesture recognition system which identifies hand gesture movement efficiently and accurately. The experimental result obtained is based on use of a single subject

Firewalls Policies Based on Software Defined Networking: A survey

Software-Defined Networking (SDN) introduces granularity, visibility and flexibility to networking, which separates the control-logic from networking devices. SDN programmatically modifies the functionality and behaviour of network devices. It separates control plane and data plane, and thus provides centralized control. Though SDN provides better performance but there are some security issues that need to be taken care of. This includes firewalls, monitoring applications, IDS(Intrusion detection systems) etc. Therefore, this research work reviews the related approaches which have been proposed by identifying their firewall scope, their practicability, their advantages and drawbacks related with SDN. This paper describes the firewall policies as the forth new security challenges.Keywords: Software defined networking, Architecture, OpenFlow, Firewalls, Anomaly detectio

Are Machine Learning Based Intrusion Detection System Always Secure?:An insight into tampered learning

Machine learning is successful in many applications including securing a network from unseen attack. The application of learning algorithm for detecting anomaly in a Network has been fundamental since few years. With increasing use of machine learning techniques it has become important to study to what extent it is good to be dependent on them. Altogether a different discipline called ‘Adversarial Learning’ have come up as a separate dimension of study. The work in this paper is to test the robustness of online machine learning based IDS to carefully crafted packets by attacker called poison packets. The objective is to observe how a remote attacker can deviate the normal behavior of machine learning based classifier in the IDS by injecting the network with carefully crafted packets externally, that may seem normal by the classification algorithm and the instance made part of its future training set. This behavior eventually can lead to a poison learning by the classification algorithm in the long run, resulting in misclassification of true attack instances. This work explores one such approach with SOM and SVM as the online learning based classification algorithms

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Plant based Biologically Inspired Intrusion Response Mechanism : An insight into the proposed model PIRIDS

Intrusion Detection Systems (IDS) are one of the primary components in keeping a network secure. They are classified into different forms based on the nature of their functionality such as Host based IDS, Network based IDS and Anomaly based IDS. However, Literature survey portrays different evasion techniques of IDS. Thus it is always important to study the responsive behavior of IDS after such failures. The state of the art shows that much work have been done on IDS on contrary to little on Intrusion Response System (IRS). In this paper we propose a model of IRS based on the inspiration derived from the functioning of defense and response mechanism in plants. The proposed model is the first attempt of its kind with the objective to develop an efficient response mechanism in a network subsequent to the failure of IDS, adopting plants as a source of inspiration

Different firewall techniques: A survey

Firewalls today are an integrated part of security mechanism of any organization or institution. Firewalls throughout the years have evolved merely from static rule mapping to understanding the dynamic behavior of traffic and react accordingly. Different firewall techniques operating in several layers of the TCP/IP model have been proposed by different authors in different times. In this paper an insight into some of the firewall techniques are discussed and also an attempt has been made to portray different challenges that exist in the application traffic

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%