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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Ganjam, Amrutha Sriya Reddy C, Kishor Kumar Radhika, D. Ganjam, Karthik Singh.t, Monika Akuthota, Keerthi Sai Stanley College of Engineering and Technology for Women abids Department of Computer Science Hyderabad India University of Edinburgh Department School of Informatics MSc Data Science Edinburgh United Kingdom Stanley College of Engineering and Technology for Women Deptartment. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331519056

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of computer Science 2025年第2期8卷 303-333页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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Intelligent Modeling Method to Optimal Predictive Model for Metal Powder Injection Molding Gears Using Fuzzy-Logic-Based Multi Objective Design

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International Journal of Fuzzy Systems 2023年第6期25卷 2338-2355页

作者： Huang, Wei-Tai Kung, Chien-Yu Chou, Jyh-Horng Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia University Taichung407 Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung807 Taiwan

This study will use intelligent modeling to optimize the metal powder injection molding gear process. The robust process combined with fuzzy theory would be used to find process parameters for optimizing process single and multiple quality characteristics. And establish a reverse transfer neural network and adaptive neuro-fuzzy system prediction model. The model hyperparameter structure will be optimized during the modeling process to improve accuracy. The industry can use it to reduce process uncertainty, cost, and extremely a process innovation. The experimental results are single-objective optimization. After optimizing the parameters, the process target displacement, volume shrinkage rate, and powder concentration, compared with the manufacturer's original process, the improvements are increased by 5.79%, 5.66%, and 1.57%. Optimizing multiple quality characteristics has risen by 5.37%, 5.66%, and 0.31%. In the prediction model building part, after the optimization experiment of hyperparameter setting, the accuracy of the backward pass neural network model can reach 96.99%;the accuracy of the adaptive neuro-fuzzy system model can reach 97.45%. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Injection molding

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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60 GHz mmWave Metasurface Superstrate for Gain and Bandwidth Improvement 3

60 GHz mmWave Metasurface Superstrate for Gain and Bandwidth...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Bilal, Asif Quddious, Abdul Kanno, Atsushi Kawanishi, Tetsuya Antoniades, Marco A. Iezekiel, Stavros University of Cyprus Emphasis Research Centre Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus Kios Research Center for Excellence Nicosia Cyprus National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Ryerson University Department of Electrical Computer & Biomedical Engineering TorontoON Canada

ISBN: (纸本)9789463968058

A multi-layer structure designed for the 60 GHz mm-wave frequency band is presented. The design consists of an antenna element on the substrate and a split ring resonator (SRR) metasurface on the superstrate. With the addition of the metasurface superstrate layer, an improved performance of the antenna in terms of gain and bandwidth is achieved. These enhancements are based on the negative index metamaterial (NIM) properties of the designed metasurface sheet. In this proposed design the bandwidth is improved from 3 GHz to 4.5 GHz, while the gain of the antenna is increased from 7.87 dBi to 13.87 dBi at 60 GHz. © 2022 URSI.

关键词： Millimeter waves

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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Continual Deep Learning on the Edge via Stochastic Local Competition among Subnetworks

arXiv

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arXiv 2024年

作者： Christophides, Theodoros Tolias, Kyriakos Chatzis, Sotirios Department of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Cyprus National Documentation Center Greece

Continual learning on edge devices poses unique challenges due to stringent resource constraints. This paper introduces a novel method that leverages stochastic competition principles to promote sparsity, significantly reducing deep network memory footprint and computational demand. Specifically, we propose deep networks that comprise blocks of units that compete locally to win the representation of each arising new task;competition takes place in a stochastic manner. This type of network organization results in sparse task-specific representations from each network layer;the sparsity pattern is obtained during training and is different among tasks. Crucially, our method sparsifies both the weights and the weight gradients, thus facilitating training on edge devices. This is performed on the grounds of winning probability for each unit in a block. During inference, the network retains only the winning unit and zeroes-out all weights pertaining to non-winning units for the task at hand. Thus, our approach is specifically tailored for deployment on edge devices, providing an efficient and scalable solution for continual learning in resource-limited environments. © 2024, CC BY.

关键词： Network layers

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Structural Equation Modeling for Stroke Risk Assessment of the Common Carotid Artery based on Texture Analysis

Structural Equation Modeling for Stroke Risk Assessment of t...

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European Conference on Electrical engineering and computer Science (EECS)

作者： Christos Loizou George Evripides Paul Christodoulides Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Faculty of Engineering and Technology Cyprus University of Technology Limassol Cyprus

The intima media thickness (IMT) of the common carotid artery (CCA) as well as texture features extracted from the intima media complex (IMC) of the CCA may be used to evaluate the prevalent clinical cardiovascular disease (CVD) and the risk of stroke. This study investigated the association between the IMT and the texture features of the IMC of the CCA and the prevalent clinical CVD using structural equation modeling (SEM). Six hundred twelve (612) longitudinal-section ultrasound images of the left and right CCA were obtained from 306 subjects (158 men and 148 women), 42 of which had clinical CVD. Each of these images was intensity normalized and despeckled. Forty (40) texture features were extracted from the IMC through a semi-automated segmentation system. To this end, we proposed a new approach on how to analyze the above data and provide an evaluation of the stroke risk. SEM is an elegant procedure employed in this study for a conceptual model of relationships between 7 different factors (unobserved constructs and observable variables) extracted from the IMC ultrasound images. The main findings of this study are: (i) Out of seven IMC texture feature groups (factors) investigated, six of them showed good fit to the conceptual model. (ii) Six hypothesized paths in the conceptual model for the impact of each texture feature group on the IMT were tested. It turned out that five of the chosen factors have a significant impact on IMT. (iii) However, as the fits of both the measurement and the structural models are not so good, the obtained results need to be improved taking certain measures in relation to the SEM. Future work will investigate the relationships between IMC texture features and IMT in relation to CVD and the carotid side.

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Metasurface Superstrate for 5G Bandwidth and Gain Enhancement

Metasurface Superstrate for 5G Bandwidth and Gain Enhancemen...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Bilal, Asif Quddious, Abdul Votsi, Haris Kanno, Atsushi Kawanishi, Tetsuya Antoniades, Marco A. Iezekiel, Stavros University of Cyprus EMPHASIS Research Centre Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus KIOS Research Center for Excellence Nicosia Cyprus National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Ryerson University Department of Electrical Computer & Biomedical Engineering TorontoON Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper discusses a dual-layer metasurface (MSF) superstrate, consisting of double split-ring resonator cells in the 28 GHz 5G mm-wave frequency band. The MSF superstrate is added above a conventional patch antenna, and an enhancement in the performance of the patch antenna in terms of bandwidth and gain is achieved. This is due to the negative index metamaterial (NIM) properties of the designed MSF superstrate. In this design, the impedance bandwidth is improved from 2.6 GHz to 3.6 GHz, while the gain of the patch antenna is increased from 6.59 dBi to 12.9 dBi. The simplicity of the design to achieve a higher gain and broader bandwidth makes it a suitable candidate for applications in 5G mm-wave communications. © 2022 IEEE.

关键词： Microstrip antennas

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

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11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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