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Design and Improvement of e-Collab Classroom as Learning Support System on Intelligent System Subject Electrical Department, Universitas Negeri Malang

Design and Improvement of e-Collab Classroom as Learning Sup...

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Vocational Education and Training (ICOVET), international conference on

作者： Anik Nur Handayani Muladi Muladi Ilham A.E. Zaeni Aripriharta Wendy Cahya Kurniawan Anie Yulistyorini Universitas Negeri Malang Malang Indonesia

ISBN: (数字)9781728181318

ISBN: (纸本)9781728181325

Practicum activity on Intelligent System requires a computer laboratory with have a Graphics Processing Unit (GPU) computer spesification to increase the speed of computing. A computer with a GPU is a high-tech and expensive device. the amount of practicum equipment needed adjusts the number of students. Especially in practicums held in parallel, the users will increase several times. In addition, this device requires assistance with training in operating procedures, and requiring longer study time. One way to overcome this problem is a cloud computing device as a practicum media. the students and lecturers interact online with practicum media, online compilers, and GPU server facilities for computing. Online classes that are collaborated with learning media are called e-Collab Classroom. the student ability of computer programming is very diverse so that it has the potential to obstruct practical activities. this is used to minimize this condition and unite the platforms used in each topic contained in the Smart Systems Course. this research improve e-Collab Classroom which can be accessed from computer browser to be easily access by students from one gate mobile application. Designing e-Collab based on mobile applications for Intelligent system subjects with the topic of practical activities, namely Convolutional Neural Network (CNN). the topic of CNN was chosen as a specific topic that represented the entire simulation of practical learning activities with cloud computing. the development of e-Collab is carried out using a prototyping model consisting of the identification of needs and objects, the identification of risks, prototype design, the experimental stage, the testing phase, and the evaluation stage. the results of the development research are the CNN jobsheet, online classroom system, and mobile application.

关键词： Internet Training Media Graphics processing units Electrical engineering Intelligent systems Java

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4th Workshop on Advances in programming Languages

2013 Federated Conference on Computer Science and Informatio...

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2013 Federated conference on Computer science and Information Systems, FedCSIS 2013 2013年 1483-1483页

作者： Lukovic, Ivan Mernik, Marjan Slivnik, Bostjan Janousek, Jan Aycock, John Chen, Haiming Henriques, Pedro Rangel Horvath, Zoltan Ivanovic, Mirjana Kardas, Geylani Kollar, J. Kosar, Tomaz Liu, Shih-Hsi Lukovic, I. Mandreoli, Federica Martínez López, Pablo E. Mernik, M. Milasinovic, Boris Moessenboeck, Hanspeter Papaspyrou, Nikolaos Pereira, Maria Joao Varanda Poruban, Jaroslav Rodriguez, Jose Luis Sierra Slivnik, B. Splawski, Zdzislaw Watson, Bruce University of Novi Sad Serbia University of Maribor Slovenia University of Ljubljana Slovenia Czech Technical University Czech Republic University of Calgary Canada Chinese Academy of Sciences China Universidade Do Minho Portugal Eotvos Lorand University Hungary Ege University International Computer Institute Turkey Technical University of Kosice Slovakia California State University United States University of Novi Sad Serbia Serbia University of Modena Italy Universidad Nacional de Quilmes Argentina University of Maribors Slovenia University of Zagreb Faculty of Electrical Engineering and Computing Croatia Johannes Kepler Universitat Linz Austria National Technical University of Athens Greece Instituto Politecnico de Braganca Portugal Universidad Complutense de Madrid Spain University of Ljubljana Slovenia Slovenia Wroclaw University of Technology Poland Stellenbosch University South Africa

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A Well-Structured Data Sharing System Implementation using EC Technology and Higher end Communication Network

A Well-Structured Data Sharing System Implementation using E...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Ashok Kumar Laith Jasim Atreyi Pramanik G. Maheswari S Sangeetha Sanjeevkumar Angadi Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India The Islamic university Najaf Iraq School of Applied and Life Sciences Division of Research and Innovation Uttaranchal University Dehradun Uttarakhand Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of EEE CMR College of Engineering & Technology Computer Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Pune Maharashtra India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Vehicular traffic networks (VANETs) are an important part of modern transportation systems, focusing on critical applications such as collision avoidance, lane change assistance, and traffic recognition. However, data distribution in VANETs is very challenging, especially due to the unique characteristics of wireless communications and the dynamic traffic environment. In this context, packet delivery control is very important for proper communication. In this paper, research proposes a new Computer defined Vehicle Network (SDVN) Controlled Routing and Scheduling (CDRS) method to solve the complex data distribution problem of VANET unlike existing approaches, CDRS uses SDVN management functions to integrate a routing strategy and a scheduling strategy that optimizes multihop data transmission. Priorities in CDRS are based on routing and scheduling constraints. Current protocols focus on finding the shortest path connecting source and destination pairs, ignoring factors such as packet proximity and link length, but performance is limited in dynamic VANET environments. Similarly, current scheduling approaches focus on single-hop data distribution and ignore the << potential benefits of multihop transmission over multiple channels. To solve these problems, CDRS formulates the data allocation problem as an integer linear programming (ILP) model that aims to maximize the number of packets sent and minimize the service interval. CDRS ensures efficient data transmission over the network by effectively allocating resources taking into account packet length, link length, and different types of channels (e.g., vehicle-to-vehicle traffic and vehicle-to-vehicle infrastructure) the simulation results show that CDRS performs better in both routing and scheduling methods. through comprehensive evaluations, CDRS continues to achieve higher delivery rates, reduced service delays, and better differentiation between real-time and virtual transactions. Additionally, CDRS has proven to b

关键词： Wireless communication Processor scheduling Simulation Vehicular ad hoc networks Transportation Routing Real-time systems

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Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm Routing

Enhancing Lifetime in Wireless Sensor Networks through Image...

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Soft Computing for Security Applications (ICSCSA), international conference on

作者： M. Ilampari M. Amina Begum N Mahesh Babu S. Gopi M. Mythreyee Shanmugavel Deivasigamani Saveetha School of Law SIMATS Department of ECE Oxford College of Engineering Chennai India Department of Information Technology Anurag Engineering College Suryapet Kodad Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of EEE Dr. MGR Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. these are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. the proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. this method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs.

关键词： Wireless sensor networks Protocols Image processing Disaster management Routing Energy efficiency Environmental monitoring Security Reliability Genetic algorithms

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EEG Signals in Epilepsy Seizure Detection: An Empirical Study

EEG Signals in Epilepsy Seizure Detection: An Empirical Stud...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： L. K. Jayashree Bondu Venkateswarlu Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

A seizure is an uncontrolled electrical disturbance in the brain that causes changes in behavior, movements or feelings, and in the levels of consciousness. the occurrence of recurrent seizure in patient indicates the presence of epilepsy, which directly affects central nervous system. the detection of Epilepsy is performed based on the Electroencephalogram (EEG) signals, which helps in recognizing the mental state of the patient suffering from the brain disorders. Since the manual detection and monitoring of electrographic seizures in continuous EEG is very time-consuming and requires a trained expert, so attempting to develop an automatic seizure detection is challenging task Various researches have been studied on Epilepsy disease using machine learning and big data concepts. Several features, classifiers, and data transformation have been used to classify seizures based on EEG signals that recognize the mental state of the patient. the features used from feature extraction, data transformation and decomposition techniques are summarized with a comparative study with respect to the performance metrics. the present research gives an overview of the seizure detection from the EEG signals and also provide challenges during seizure determination. A comparison is made among the state-of-art techniques and the algorithms that are involved in the Epilepsy seizure determination. the comparative analysis for the Epilepsy seizure determination was evaluated by means of accuracy, precision, sensitivity, and specificity. At last, an efficient emprical mode decomposition (EMD) algorithm is used for seizure detection to overcome the limitations faced in existing research and clearly explained in this research.

关键词： Epilepsy Sensitivity and specificity Big Data Feature extraction Electroencephalography Task analysis Principal component analysis

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Shape Matching Based on the Enhancement of Riemannian Structure Information 2020

Shape Matching Based on the Enhancement of Riemannian Struct...

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Proceedings of the 2020 4th international conference on Vision, Image and Signal Processing

作者： Lichao Cui Wenshuang Zhao Muhammad Umair Hassan Irfan Yaqoob Department of Information Engineering Yantai Vocational College China Department of Digital Media and Creative Design Sichuan University of Media and Communications China Department of ICT and Natural Sciences Norwegian University of Science and Technology Norway School of Information science and Engineering University of Jinan China

ISBN: (纸本)9781450389532

In this paper, we propose an automatic shape matching method which can be used to match two non-rigid deformation shapes with isometric or nearly isometric transformations. the main purpose of our method is to enhance the effectiveness of the linear assignment matrix, and then obtain the final matching result by using a combinatorial optimization algorithm to solve the objective function. First, we calculate each point in the two matching shapes separately by using SHOT and HKS descriptors. then, we combine two assignment matrices constructed by the SHOT and HKS descriptors, which combination can fully capture the Euclidean structure information and Riemann structure information of the graph and effectively enhances the relationship between points. In the final, we construct our new objective function by using descriptors and heat distribution matrix, all of which are enhanced by Riemann structure information. the final matching result is obtained by solving the objective function using the projected descent optimization procedure. In light of this, our algorithm is better than other state-of-the-art methods. the effectiveness of this method is proven by geodesic error distance statistics from two commonly used datasets with ground truth.

关键词： Shape Matching Shape Corresponding Riemannian Structure Information

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Identifying Influencers in thai Internet Forum based on Topic-oriented Gravity Model 2020

Identifying Influencers in Thai Internet Forum based on Topi...

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2020 4th international conference on Computer science and artificial Intelligence

作者： Jirateep Tantisuwankul Bundit Manaskasemsak Arnon Rungsawang Department of Computer Engineering Faculty of Engineering Kasetsart University Thailand

ISBN: (纸本)9781450388436

the task of identifying influencers provides a lot of benefits for various practical applications such as recommendation systems, viral marketing, and information monitoring. this issue can traditionally be solved via a network structure with several proposed graph algorithms. However, most of them employ a global computation with much time-consuming; some consider only undirected and unweighted networks which may be inconsistent with the nature of data. Inspired by the law of gravity in Physics, we present the Topic-oriented Gravity Model (TopicGM) that investigates a directed and weighted network incorporating users' topical aspects. the key concept is that an individual is first represented as a textual content he created or read. Afterwards, TopicGM simply adopts a topic modeling, i.e., the Hierarchical Dirichlet Process (HDP), to classify topics over those contents. A topical network is then constructed where nodes represent individuals and an edge connects two individuals in the direction from the poster to the reader with a topical confidence weight. Finally, we apply the gravity formula to calculate influence scores and rank individuals. the experimental results, conducted on real-world data gathered from *** (famous thai web forum), show that our approach outperforms many state-of-the-art baselines by accurately identifying influencers within the top of rankings.

关键词： viral marketing gravity model influencer identification social network topic model

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Robust Multiple Kernel Subspace Clustering Based on Low Rank Consensus Kernel Learning 20

Robust Multiple Kernel Subspace Clustering Based on Low Rank...

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Proceedings of the 2020 4th international conference on Electronic Information Technology and Computer engineering

作者： Qin Liang Xiaoqian Zhang Liang Luo School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781450387811

Subspace clustering method based on kernel learning shows superior performance when dealing with nonlinear high dimensional data. However, the clustering performance of existing single kernel subspace clustering methods largely depends on the selected kernel function, and the low-rank structure of the data in the feature space after kernel mapping is not considered. In addition, the learned affinity matrix cannot maintain the block diagonal property, which may reduce the clustering performance. In this paper, we propose a robust multiple kernel subspace clustering based on low rank consensus kernel learning (MKLRSC) method for data clustering. Our model has three innovations: (1) the introduction of correntropy in the multiple kernel weighting strategy helps to learn the optimal consensus kernel. (2) In order to maintain the low-rank structure of input data, we impose a nuclear norm constraint on the optimal consensus kernel matrix. (3) Considering the block diagonal property of the affinity matrix, MKLRSC applies block diagonal constraint to the coefficient matrix. Compared with several state-of-the-art multiple kernel subspace clustering methods, experiments on three datasets confirm that MKLRSC achieves more competitive clustering results.

关键词： Low-rank kernel Multiple kernel learning Block diagonal regularizer Subspace clustering

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A Deep Learning Algorithm Improvement for Brain Tumour Segmentation Using Kernel-Based CNN and M-SVM

A Deep Learning Algorithm Improvement for Brain Tumour Segme...

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Advancement in Electronics & Communication engineering (AECE), international conference on

作者： Nishant Kumar Singh Pushpneel Verma Department of Computer Science & Engineering Bhagwant University Ajmer Rajasthan India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Recently, medicinal imaging has shown significant growth in demand due to its ability to provide useful information from human anatomical images, which helps understand symptoms and diagnose patients (Garzón & Cardenas-Villa 2020), with astrocytoma’s separation among the crucial tasks popular the field that can assist clinicians on diagnosis, treatment planning as well as monitoring of patient they are concerned about. Deep learning algorithms have started to show impressive results in the segmentation of brain tumors from MRI images over recent years. therefore, this study presents a new deep learning algorithm that utilizes kernel-based Convolutional Neural Networks (CNN) and multi-class Support Vector Machines (M-SVM) for more correct separation of astrocytoma’s. the latter is a kernel-based CNN method that efficiently learns deep structures as of the contribution MRI pictures. the features remain input into the mm classifier, which accurately classifies them to different tumor regions (necrotic core, edema and enhancing tumor). Besides, we introduce a boundary refinement module into the framework to fine-tune results at segmentation boundaries for better accuracy. Here, we attempted to analyze our analyze algorithm over publicly available annotation datasets experimentally and compared them against the state-of-the-art as baselines. Our approach outperformed previous studies with an normal Dice comparation constant of 0.93, illustrating the capacity to segment brain tumor regions accurately. In addition, our algorithm demonstrated a resilience to noise and differences in image resolution.

关键词： Deep learning Support vector machines Image segmentation Accuracy Magnetic resonance imaging Brain tumors Classification algorithms Convolutional neural networks Medical diagnostic imaging Overfitting

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Network Model for Improved Localization Performance in UWSN:A Node Deployment Perceptive

Network Model for Improved Localization Performance in UWSN:...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： T. T. Manikandan Rajeev Sukumaran M. R. Christhu Raj M. Saravanan SRM Institute of Science and Technology Chennai India M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Underwater Wireless Sensor Networks (UWSNs), is one of the stat-of-the-art research areas if the present day oceanography domain is concerned due to the kind of support it offers for the wide range of underwater applications. Due to the drastic increase in the number of potential underwater applications the effective communication between different underwater nodes need to be ensured. But when putting in place an Underwater Wireless Sensor Networks, one of the most important tasks is the deployment of nodes, which plays an important role in achieving seamless routing in the tricky underwater environment. Because only based on the node deployment, most of the essential network services such as network boundary detection, routing, and topology control of network are carried out. A huge amount of research is done with respect to 2D node deployments in terrestrial networks, but the amount of attention given to 3D node deployments are very limited. So the scope of this article revolves around analyzing the effects of node deployment techniques on the localization performance of the UWSN environment which is 3 dimensional in nature. this research work has considered three different node deployment strategies such as regular tetrahedron, cube, and random node deployment techniques for conducting simulation study. As a result of the simulation study carried out in this research article, it is clear that the performance of regular tetrahedron node deployment is a way better than cube node deployment and random node deployment techniques. Since most of the present day applications use random node deployment, the results of this article may lead to the improved design of UWSN in the upcoming years.

关键词： Mathematical model Topology Routing Wireless sensor networks Temperature measurement Salinity (geophysical) Propagation losses

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