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2012 International Conference on Telecommunications and Multimedia, TEMU 2012

作者： Stratakis, D. Miaoudakis, A. Yioultsis, T. Xenos, T. Department of Applied Informatics and Multimedia Technological Educational Institute of Crete Heraclion Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781467327817

Wireless networking technologies in recent years have witnessed important developments since the advantages benefit both data transmission service providers, and individuals or ordinary users. Ease of installation of a wireless network, led to the development of millions of Wi-Fi networks throughout the world. Nevertheless, Wi-Fi has certain drawbacks that inspired the implementation of a new technology called WiMAX. In 2003, the IEEE 802.16™ standard, also known as WiMAX (Worldwide Interoperability for Microwave Access) was adopted, in order to meet the requirements for broadband wireless access. The purpose of this paper is to study how the electromagnetic fields generated by WiMAX base stations can be evaluated in terms of exposure. © 2012 IEEE.

关键词： Wi-Fi

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Domain-Specific Conceptual Modeling 1

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1000年

作者： Dimitris Karagiannis Heinrich C. Mayr John Mylopoulos

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Novel Noise Filter Techniques and Dynamic Ensemble Selection for Classification

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Recent Advances in computer Science and Communications 2022年第1期15卷 48-59页

作者： Nguyen, Thi Ngoc A. Nhu, Quynh P. Solanki, Vijender K. School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam CMC Institute of Science and Technology 11 Duy Tan Cau Giay Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. Objective: In this paper, novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improves the performance of classification. Methods: The proposed NF-DES model, which was demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. Results: The results show that our proposed model is better than other models. Conclusion: The novel noise filer and dynamic ensemble learning with the aim to improve the classification ability are presented. To improve the performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning chooses the appropriate subset classifiers in the pool of base classifiers. © 2022 Bentham Science Publishers.

关键词： Support vector machines

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Retraction Note: Multiclass skin lesion classification using deep learning networks optimal information fusion

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Discover applied Sciences 2025年第1期7卷 1-1页

作者： Khan, Muhammad Attique Hamza, Ameer Shabaz, Mohammad Kadry, Seifeine Rubab, Saddaf Bilal, Muhammad Abdullah Akbar, Muhammad Naeem Kesavan, Suresh Manic Department of Computer Science HITEC University Taxila Pakistan Model Institute of Engineering and Technology Jammu India Department of Applied Data Science Noroff University College Oslo Norway Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of CS SEECS NUST Islamabad Pakistan National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Communication Engineering National University of Science and Technology Muscat Sultanate of Oman

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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Taxonomy development and its impact on a self-learning e-recruitment system

Taxonomy development and its impact on a self-learning e-rec...

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8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012

作者： Faliagka, Evanthia Karydis, Ioannis Rigou, Maria Sioutas, Spyros Tsakalidis, Athanasios Tzimas, Giannis Computer Engineering and Informatics Dept. University of Patras Patras Greece Dept. of Applied Informatics in Management and Economy Technological Educational Institute of Messolonghi Messolonghi Greece Dept. of Informatics Ionian University 49100 Kerkyra Greece

ISBN: (纸本)9783642334085

In this work we present a novel approach for evaluating job applicants in online recruitment systems, using machine learning algorithms to solve the candidate ranking problem and performing semantic matching techniques. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants' LinkedIn profile, and compares them semantically to the job's prerequisites. It also infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters, thus it can be trusted for the automation of applicant ranking and personality mining. © 2012 IFIP International Federation for Information Processing.

关键词： Data mining

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Best Selection of Generative Adversarial Networks Hyper-Parameters Using Genetic Algorithm

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SN computer Science 2021年第4期2卷 283页

作者： Alarsan, Fajr Ibrahem Younes, Mamoon Informatics and Decision Supporting Systems Higher Institute for Applied Sciences and Technology Damascus Syrian Arab Republic Faculty of Computer and Automation Engineering Damascus University Damascus Syrian Arab Republic

Generative adversarial networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other. In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digit numbers 1–9 with generator and train discriminator to decide whereas the generated images are fake or true. Genetic algorithm (GA) technique was used to adapt GAN hyper-parameters, the resulted algorithm is named GANGA: generative adversarial network with genetic algorithm. The resulted algorithm has achieved high performance;it was able to get zero value of loss function for the generator and discriminator separately. Anaconda environment with tensorflow library facilitates was used;python as programming language was adapted with needed libraries. The implementation was done using MNIST dataset to validate the work. The proposed method is to let genetic algorithm choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function used to find best values of learning rate, batch normalization, number of neurons and a parameter of dropout layer. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Discriminator Generative adversarial networks (GAN) Generator Genetic algorithm Image synthesis MNIST dataset

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Image feature point matching for indoor positioning

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2017 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2017

作者： Chuang, Chi-Hung Chen, Ying-Nong Fan, Kuo-Chin Dept. of Applied Informatics Fo Guang University ILan Taiwan Institute of Computer Science and Information Engineering National Central University Chung-Li Taiwan

ISBN: (纸本)1601324642

With the development of technology and the popularity of smart phones, people have become more considerate about the convenience in life of the disabilities and thus add lots of assistive equipment. Portable equipment such as smart phones and tablet are daily necessities for most of the people. However, there are few applications that are designed for the visual impairments and the elders. The proposed system can be used indoor to get the location of the user and plan the path. Via matching the image features, it will recognize where the user is. The purpose of this is to help the visual impairments or the elders to navigate to their destinations. In addition, it can also use robot to get the position by matching image features. The proposed system uses images captured from smart phones and are compared with database, then it will return the most similar position back to the user. User then can obtain some useful information of the surroundings such as the location of toilet or elevator, which will help user planning the path to destinations. Scale-invariant feature transform (SIFT) is used in this thesis. Via image features matching with pre-established scenes image features database. FLANN (Fast Library for Approximate Nearest Neighbors) is applied to build randomized k-d trees. The k-d tree can create an index for SIFT’s descriptor, which can speed up feature matching. Experimental results in the proposed method can achieve a good feasbilitu. CSREA Press ©

关键词： Nearest neighbor search

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

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8th International Conference on computer Technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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AIANTAS) for IoT Cyberspace - An Upcoming Cloud-based Security Architecture for Police Authorities 9

AIANTAS) for IoT Cyberspace - An Upcoming Cloud-based Securi...

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9th IEEE International Conference on Information, Communication and Networks, ICICN 2021

作者： Memos, Vasileios A. Psannis, Konstantinos E. Ishibashi, Yutaka University of Macedonia School of Information Sciences Dept of Applied Informatics Thessaloniki Greece Nagoya Institute of Technology School of Engineering Dept of Computer Science Nagoya Japan

ISBN: (纸本)9780738113456

In this research work, we investigate the effects of cybercrime and focus on concerns about cybercrime expansion. Since black-hat hackers are constantly one step ahead of white-hat hackers and exploit new sophisticated hack-tools to intrude to devices and networks to commit cybercrime for different reasons, the utilization of the available emerging technologies can provide better privacy and protection level, while they can be used to combat cybercriminal attacks. This shift to new technologies and methods is regarded to be an urgent necessity today. Therefore, we demonstrate a potential novel security mechanism for Police Authorities, named 'Artificial Intelligence ANTi-Attack System' or simply 'AIANTAS' that aspires to improve the detection rate of cybercriminals and lead to their faster arrest. This model can be integrated into the other National Security defense mechanisms both in Greece and worldwide. © 2021 IEEE.

关键词： Data Analytics

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