International Conference on Information and Communication Technology 

About: International Conference on Information and Communication Technology is an academic conference. The conference publishes majorly in the area(s): Cloud computing & Feature extraction. Over the lifetime, 3433 publications have been published by the conference receiving 18571 citations.

Large-Scale Video Classification with Convolutional Neural Networks

Abstract: Convolutional neural networks have been established as an unbelievable class of models for picture confirmation issues. Enabled by these results, we give CNN’s extensive trial evaluation a large degree of video-action syllabus using another dataset of 8M YouTube accounts. To get the Chronicles and its effects, we’ve used a YouTube video specification framework, which gives the names of the accounts they focus on. While the names are machine-generated, they are high-precision and are derived from a group of human-based icons, including metadata and question click signals. We have filtered the video names (Knowledge Graph Components) using both modern and manual curation strategies, including curiosity regarding whether the print is clearly indisputable. After that, we decode each video at one-layout per-second and use the deep CNN adjusted to ImageNet to remove the cover depicted immediately before the course of the action layer. Finally, we’ve stuffed the packaging features and made available both features and video level names for download. We train unique (ambiguous) game plan models on the dataset, survey them using significant evaluation estimates, and report them as baseline. Regardless of the size of the dataset, a portion of our models train the connection in less than a day on a singular machine using VGG. CNN our course release code for setting up model deals and generating predictions.

28/38-GHz dual-band millimeter wave SIW array antenna with EBG structures for 5G applications

Abstract: The design of linearly polarized dual-band substrate integrated waveguide (SIW) antenna/array operating at Ka-band is proposed. The single antenna element consists of a SIW cavity with two longitudinal slots engraved in one of the conducting planes. The longer and shorter slots are resonating at 28 GHz and 38 GHz, respectively. Only the simulated results are presented. All simulations have been carried out using industry-standard software, CST Microwave Studio. For single antenna element, an impedance bandwidth (S11< −10 dB) of 0.45 GHz (1.60 %) and 2.20 GHz (5.8 %) is achieved with the maximum gain of 5.2 dBi and 5.9 dBi at 28 GHz and 38 GHz, respectively. To achieve high gain, a horizontally polarized linear array of four elements (1 × 4) is designed. For the antenna array, a microstrip lines feed network is designed using 3-dB wilkinson power divider. At 28 GHz and 38 GHz, the impedance bandwidth is 0.32 GHz (1.14 %) and 1.9 GHz (5%) having maximum gain of 11.9 dBi and 11.2 dBi, respectively. A low loss/cost substrate, RT/Duroid 5880 is used in the proposed designs.

Smart home system using android application

Abstract: This paper presents the overall design of Home Automation System (HAS) with low cost and wireless remote control. This system is designed to assist and provide support in order to fulfil the needs of elderly and disabled in home. Also, the smart home concept in the system improves the standard living at home. The main control system implements wireless Bluetooth technology to provide remote access from PC/laptop or smart phone. The design remains the existing electrical switches and provides more safety control on the switches with low voltage activating method. The switches status is synchronized in all the control system whereby every user interface indicates the real time existing switches status. The system intended to control electrical appliances and devices in house with relatively low cost design, user-friendly interface and ease of installation.

A genetic algorithm for power-aware virtual machine allocation in private cloud

Abstract: Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.

Decision Tree Based Intrusion Detection System for NSL-KDD Dataset

Abstract: In this paper, Decision Tree (DT) based IDS is proposed for NSL-KDD dataset. The proposed work uses Correlation Feature Selection (CFS) subset evaluation method for feature selection. Feature selection improves the prediction performance of DT based IDS. Performance is evaluated before feature selection and after feature selection for five class classification (normal and types of attack) and binary class classification (normal and attack). The obtained result is compared and analyzed with the other reported techniques. The analysis shows that the proposed DT based IDS provides high DR and accuracy. The overall result for binary class classification for the dataset is higher than five class classification.