Autism Spectrum Disorder (ASD) starts showing symptoms in the early formative years of an individual, affecting brain development and negatively impacting social and communication skills. Subjective diagnostic methods for ASD detection require lengthy questionnaires, trained medical personnel, and occupational therapists, and are subject to observer variability. Recent years have seen a rise in the usage of machine learning techniques for detecting ASD, which stems from a requirement for objective and accurate detection methods. This research analyzes the performance of various deep convolutional architectures for the detection of ASD. The primary objective of this work is to select a method capable of performing automatic feature extraction and classification with a relatively high degree of accuracy. Several experiments were conducted with different stateof-the-art deep architectures, out of which the ResNet50 performed the best, with an average accuracy of 81%. The performances of these architectures were analyzed in terms of precision, recall, and accuracy.
In this work, the continue from the last research work done [20], thus it is proposed a data mining based anomaly detection system, aiming to detect volume anomalies, using Simple Network Management Protocol (SNMP) monitoring. The method is novel in terms of combining the use of Digital Signature of Network Segment (DSNS) with the evolutionary technique called Particle Swarm Optimization (PSO) [5] and neural network training, applied in a real data set. PSO is a high efficient heuristic technique with low computational complexity, developed in 1995 by Kennedy and Eberhart [1] inspired by social behavior of bird flocking. The DSNS is a baseline that consists of different normal behavior profiles to a specific network device or segment, generated by the GBA tool (Automatic Backbone Management), using data collected from SNMP objects. The proposed anomaly detection system uses the SVM in order to clusterize the traffic collected by SNMP agents and its respective DSNS. The PSO is combined with the SVM in order to improve performance and quality of the solution in the clusterization and calculation of clusters centroids. Tests were carried out using a real network environment in the Techno India University, Kolkata. Numerical results have been shown that the obtained detection and false alarm rates are promising. It is also implemented the deterministic method proposed in order to detect anomalies on the same dataset, so that both methods could be compared.
In this paper, a resource management technique is proposed to handle the request of Virtual Machines (VM's) as per the need of users, consisting of resources (mips, vm image size, network bandwidth, number of cpu's) containing cloudlets which in turn are cloud-based application services (content delivery, social networks) commonly deployed in data centers. There are three priority mechanisms governing the user's requests namely low, medium and high priority requests. Here, the priority is given to the amount of VM's requested. To implement the above concept, the Hybrid Cloud model is used by which the benefits of both the private and public clouds can be reaped. This model has its advantages that it proves to be cost-effective as the resources are effectively utilized from private clouds and only when exhausted are taken from public clouds which is cheaper. General TermsCloud Computing, Distributed and Parallel Systems.
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