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Application of an Improved Genetic algorithm in Tourism Route Planning 3rd

Application of an Improved Genetic Algorithm in Tourism Rout...

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3rd international conference on Wireless Communications and Applications,ICWCA 2019

作者： Chen, Xiaoyan Zhang, Kun Wang, Haifeng Chen, Jing Aslam, Bhatti Uzair College of Computer Science and Technology Hainan Tropical Ocean University Sanya572022 China Education Center of MTA Hainan Tropical Ocean University Sanya China College of Computer Science and Information Technology Nanjing Normal University NanjingJiangsu210023 China

ISBN: (纸本)9789811556968

In this paper, after determining the needs of the users, the mathematical model of travel planning is established, and the time and cost constraints are studied. The traveling salesman problem (TSP), from the dimension of time and cost, is defined as the objective function. The basic genetic algorithm is studied and is put forward an improved genetic algorithm. The simulation result shows that the convergence speed of the improved genetic algorithm is better than genetic algorithm (GA). Finally, the application of tourism planning path in the field of wireless network and big data emerging technology and its good meaning are shown. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wireless networks

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12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

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12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis;membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization;membership-Mappings for Data Representation Learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep Learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep Learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep Learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the Machine Learning algorithm for Gene Expression Data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep Learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving Machine Learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in simulation Graphs;Walk Extraction Strategies for Node Embeddings with rdF2Vec in Knowledge Graphs;bridging Semantic Web and Machine Learning: First Results of a Systematic Mapping Study.

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Optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees algorithm 3rd

Optimizing the Classification of Network Intrusion Detection...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

作者： Mebawondu, Olamatanmi J. Alowolodu, Olufunso D. Adetunmbi, Adebayo O. Mebawondu, Jacob O. Department of Computer Science Federal Polytechnic Nasarawa Nigeria Department of Computer Science Federal University of Technology Akure Nigeria Department of Cyber Security Federal University of Technology Akure Nigeria

ISBN: (纸本)9783030691424

Over the years, the vulnerability of the network system has completely revolutionized the security system. Attackers simply exploit these exposures to gain undue access to network resources. It is essential to safeguard network resources with the Intrusion Prevention System (IPS) and Intrusion Detection System (IDS). Hence, to develop an optimized IDS model using ensemble modeling of machine learning technique is paramount. Therefore, this research attempts to build a network IDS using an ensemble of Decision Trees (DT) algorithms to optimize an IDS model. Furthermore, the C4.5 DT classifier used on the University of New South Wales Network-Based 2015 (UNSW-NB15) network data set. Then the ensemble techniques of bagging and AdaBoost are compared during the performance evaluation. The results of this work depict that performance increase as training size increases. Consequently, the results showed that adopting the ensemble model for the C4.5 decision tree classified improved network intrusion classification compared to using the machine learning algorithm in isolation. Moreover, the result also showed an improved classification model for network IDS model. The AdaBoost ensemble model of the C4.5 DT algorithm using partitions of 90% of training and 10% of testing data sets performs the best with 98% accuracy and precision. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

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Representing a Quantum Fourier Transform, Based on a Discrete Model of a Quantum-Mechanical System 1

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3rd international conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2020

作者： Shalagin, Sergei Computer Systems Department Kazan National Research Technical University named after A.N. Tupolev – KAI Karl Marks Street 10. Kazan420111 Russia

ISBN: (数字)9789811614835

ISBN: (纸本)9789811614828

This paper presents a discrete model of the quantum fast Fourier transform (QFFT) defined over a quantum mechanical system that includes N basis states (QMS(N)). In this case, the QMS(N) is defined based on a known discrete model described using 2(N − 1) parameters, of which (N − 1) parameter describes the amplitude components of the basic states of the QMS(N), and (N − 1) parameter describes the phase components. QFFT is modelled discretely by specifying the same type of operations, which are described as the known quantum gates: A single-qubit Hadamard gate and a two-qubit gate with a controlled phase. Each of these valves affects the specified parameters of the QMS(N) N/2 times. This representation allows you to display the impact of the QFFT on the QMS(N) by varying only N of 2(N − 1) of its parameters. In addition, simulation of the effects of N/2 Hadamard gates and N/2 controlled-phase gates on the specified parameters of the discrete QMS(N) model can be performed in parallel. This circumstance allows us to perform distributed modeling of the impact of QFFT on the QMS(N) when using multiprocessor computing systems, both existing and prospective, both specialized and general-purpose. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Fast Fourier transforms

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Routing in Internet of Things Using Cellular Automata 3rd

Routing in Internet of Things Using Cellular Automata

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3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Heidari, Ehsan Movaghar, Ali Motameni, Homayun Homayun, Esmaeil Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Doroud Branch Islamic Azad University Doroud Iran

ISBN: (纸本)9789811551123

The Internet of Things (IoT) is a new model that contributes to connect heterogeneous objects and devices. It can share information with any device globally. Furthermore, the sensor technologies, communications, networks, and cloud computing technology in the IoT are expected to be integrated into large-scale monitored spaces. The main infrastructure of monitoring in the IoT is based on wireless sensor networks. Saving energy and expanding the lifetime are significant properties of the sensor’s nodes in these systems. The efficient energy saving projects should develop appropriate energy consumption’s algorithms to improve the lifetime of the networks in the IoT systems. In this paper, a routing algorithm is proposed to calculate the distributed cluster using cellular learning automata technology, which aims to reduce energy consumptions and can efficiently increase the network lifetime in the IoT system. The key objective of this project is reducing the data transmission distance from sensor nodes using the properties of the cluster structure. According to the cluster architecture, the data transmission distance between the sensors nodes can be reduced by balancing the network load, thereby reducing energy consumption and, consequently, enhancing the network lifetime. simulation experiments revealed the superiority of this approach in the IoT to the previous ones with regard to energy consumptions and network lifetime. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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Optimization and Transmit Power Control Based on Deep Learning with Inaccurate Channel Information in Underlay Cognitive Radio Network 3

Optimization and Transmit Power Control Based on Deep Learni...

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2020 3rd international conference on modeling, simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Zong, Xuekai Zhu, Xiaomei Zhu, Aichun Byrne, John Oliver College of Computer Science and Technology Nanjing Tech University Nanjing211816 China

In cognitive radio network(CRN), power control often faces the complicated iterations and large calculations, resulting in poor real time performance of the system. In this paper, a deep learning-based power control is proposed for CRNs, where the secondary users (SUs) can share the same channel of primary users (PUs) without causing excessive interference to the communication of PU. In the novel scheme, the DNN model is used to treat the input and output of the power control algorithm as unknown non-linear mappings and fit them, which determines the proportion of transmit power allocated to each SU, considering the interference caused to the PU. With this scheme, the maximization of the SUs sum-rate can be achieved. Furthermore, due to some errors in the practical samples of channel information, an auto-encoder is used to compress the channel coefficient through an encoder and reconstruct them through a decoder before DNN training. The simulations results show that the power control method using a combination of auto-encoding and DNN can improve the real-Time performance of the system. And the sum-rate of SU is improved while the interference caused to the PU can be regulated even with the inaccurate channel information. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Feature Weighting and Classification modeling for Network Intrusion Detection Using Machine Learning algorithms 3rd

Feature Weighting and Classification Modeling for Network In...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

作者： Mebawondu, Olamatanmi J. Adetunmbi, Adebayo O. Mebawondu, Jacob O. Alowolodu, Olufunso D. Department of Computer Science Federal University of Technology Akure Nigeria Department of Computer Science Federal Polytechnic Nasarawa Nigeria Department of Cyber Security Federal University of Technology Akure Nigeria

ISBN: (纸本)9783030691424

Globally, as the upsurge in dependencies on computer network services, so are the activities of attackers that gain undue access to network resources for selfish interest at the expense of the stakeholders. Attackers threaten integrity, availability, and confidentiality of network resources despite various preventive security measures, hence the need to study ways to detect and minimize attackers’ activities. This paper develops a Network-Based Intrusion Detection System (NBIDS) using the machine learning algorithms capable of detecting and preventing anomaly (attack) network traffic from the Internet, thereby reducing cases of successful network attacks. Gain Ratio and Information Gain are used for features ranking on the UNSW-NB15 benchmark network intrusion dataset. The first fifteen highly ranked features are selected for developing classification models using C4.5 and Naïve Bayes (NB), coincidentally the two feature ranking approaches to select the same features but in a different ordering. Empirical results show that the C4.5 algorithm outperformed NB for all simulations based on a different spilled ratio of testing and training sets as it returns the highest accuracy of 90.44% against 75.09% for NB for two-class models on simulation IV. For all the experimental setup, both DT and NB have a constant precision of 91% to 75%, the True Positive value of 90%:75%, and False Positive value of 8.6%:22.8%, respectively. The experiments revealed that accuracy increases as the training ratio increases. The results show that the approach is practicable for real-time network intrusion detection. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

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2nd international conference on Artificial Intelligence in HCI, AI-HCI 2021, Held as Part of the 23rd HCI international conference, HCII 2021

2nd International Conference on Artificial Intelligence in H...

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2nd international conference on Artificial Intelligence in HCI, AI-HCI 2021, Held as Part of the 23rd HCI international conference, HCII 2021

ISBN: (纸本)9783030777715

The proceedings contain 36 papers. The special focus in this conference is on Artificial Intelligence in HCI. The topics include: How Intuitive Is It? Comparing Metrics for Attitudes in Argumentation with a Human Baseline;What Does It Mean to Explain? A User-Centered Study on AI Explainability;Morality Beyond the Lines: Detecting Moral Sentiment Using AI-Generated Synthetic Context;designing a Gender-Inclusive Conversational Agent For Pair Programming: An Empirical Investigation;estimation of Learners’ Engagement Using Face and Body Features by Transfer Learning;toward Automated Mixed Reality Interface Design: An Evolutionary Optimization Approach;building Conversational Agents for Military Training: Towards a Virtual Wingman;natural Interaction with Traffic Control Cameras Through Multimodal Interfaces;why Developing simulation Capabilities Promotes Sustainable Adaptation to Climate Change;towards an Ambient Smart City: Using Augmented Reality to Geospatially Filter the Right Airbnb via Artificial Intelligence;Disentangling Trust and Anthropomorphism Toward the Design of Human-Centered AI Systems;Assisting Text Localization and Transcreation Tasks Using AI-Based Masked Language modeling;KLSI Methods for Human Simultaneous Interpretation and Towards Building a Simultaneous Machine Translation System Reflecting the KLSI Methods;investigating Viewer’s Reliance on Captions Based on Gaze Information;pet Dogs’ and Their Owners’ Reactions Toward Four Differently Shaped Speaking Agents: A Report on Qualitative Results in a Pilot Test;a Questionnaire Data Clustering Method Based on Optimized K-Modes algorithm;Automatic Generation of Machine Learning Synthetic Data Using ROS;visual Prediction Based on Photorealistic Style Transfer;Towards Design Principles for User-Centric Explainable AI in Fraud Detection;HCD3A: An HCD Model to Design Data-Driven Apps;foreword.

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Multipath routing using improved grey wolf optimizer (igwo)-based ad hoc on-demand distance vector routing (aodv) algorithm on manet 3rd

Multipath routing using improved grey wolf optimizer (igwo)-...

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Chaturvedi, Abhay Kumar, Rakesh Department of Electronics and Communication Engineering GLA University MathuraUP281406 India Department of Computer Engineering and Applications GLA University MathuraUP281406 India

ISBN: (纸本)9789811553448

Mobile ad hoc network (MANET) is type of a wireless network without any infrastructure in which each node has the capability to seek out the best route. In the proposed system, improved grey wolf optimizer (IGWO)-based AODV protocol is proposed. It is used to optimize the AODV parameters for providing better energy consumption nodes along with multiple best routing paths. The proposed method contains three phases such as network model, AODV routing protocol, and generation of objective function in IGWO-based AODV. In the network model, nodes are connected to send and receive the packets. In the second phase, the AODV protocol is focused to calculate the energy utilization through distance among nodes. In the third phase, the IGWO is used to optimize the parameters such as energy, hop count, throughput, and delay using the fitness function values. The optimized search criteria of GWO help to decrease amount of time for the search process. Thus, the simulation results conclude that the proposed IGWO-AODV algorithm is better than the existing methods in terms of lower energy utilization, excellent throughput, relatively lesser end-to-end delay, and better network lifespan. © Springer Nature Singapore Pte Ltd 2021.

关键词： Energy utilization

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

ISBN: (纸本)9783030691424

The proceedings contain 55 papers. The special focus in this conference is on Information and Communication Technology and Applications. The topics include: Application of Supervised Machine Learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems;anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers;Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF;A Conceptual Hybrid Model of Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) for Masquerade Attack Detection;An Automated Framework for Swift Lecture Evaluation Using Speech Recognition and NLP;DeepFacematch: A Convolutional Neural Network Model for Contactless Attendance on e-SIWES Portal;hausa Intelligence Chatbot System;an Empirical Study to Investigate Data Sampling Techniques for Improving Code-Smell Prediction Using Imbalanced Data;a Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection Value;application of Big Data Analytics for Improving Learning Process in Technical Vocational Education and Training;validation of Student Psychological Player Types for Game-Based Learning in University Math Lectures;outlier Detection in Multivariate Time Series Data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score;an Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational Intelligence;optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees algorithm;identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features;feature Weighting and Classification modeling for Network Intrusion Detection Using Machine Learning algorithms;comparative Performance Analysis of Anti-virus Software.

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