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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Xin-Jin Kek Yu-Beng Leau Soo Fun Tan Cybersecurity Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In the current context of research, it investigates user authentication through keystroke dynamics using the neural network model which focuses on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short-Term (LSTM). As of above, four distinct experiments were conducted: (1) evaluating 3 different algorithms, (2) layering configuration for CNN and GRU fusion, (3) number of filters and neurons as hyperparameters, (4) assessing combinations of events counts and classes. In justification, the final outcome demonstrates an achieved accuracy of 87%. Based on the experiments that were carried out, optimal hyperparameters were identified: window size of 40, 256 for the number of filters and neurons and 2500 as the optimal number of events with 30 as the number of classes. These findings underscore a significance of considering diverse combinations of a number of events and classes by providing insights into the determination of optimal hyperparameters for keystroke dynamics in performance evaluation.

关键词： Performance evaluation Accuracy Filters Sensitivity Keystroke dynamics Neurons Authentication Convolutional neural networks Long short term memory Biological neural networks

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A COMPREHENSIVE FRAMEWORK FOR EVALUATING AUTONOMOUS VEHICLES IN SMART AND SUSTAINABLE URBAN TRANSPORTATION 51

A COMPREHENSIVE FRAMEWORK FOR EVALUATING AUTONOMOUS VEHICLES...

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51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Mohamed, Mona Elsayed, Assmaa Sallam, Karam M. Radwan, Ibrahim Abdel-Basset, Mohamed Higher Technological Institute 10th of Ramadan City44629 Egypt Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Collage of Computing and Informatics University of Sharjah Sharjah United Arab Emirates University of Canberra Canberra Australia

Recently, a plethora of thoughts and terminologies have surfaced intending to protect the environment and public health as sustainability and smart cities. Wherein the relationship between these terminologies has an explicit link. The implementation of smart city regulations has the potential to enhance the standard of living for citizens by incorporating sustainable practices. Smart and green urban transportation is one of the sustainable strategies and practices. The newest innovations in Smart and ecological urban transportation are autonomous vehicles (AuVs), which can move around with minimal help from humans. Thanks to AuVs the transportation costs, pollution in cities, and dramatically the number of accidents and fatalities on the roads have lessened. Also, all social groups have better access. Opting for AuV which is optimal, and efficient is one of the toughest obstacles. This process is conducted based on a set of criteria. Hence, Multicriteria Decision Making (MCDM) techniques contribute to constructing a three-tiered decision-making paradigm (TTDM) for appraising AuV and recommending optimal AuV. Each tier is responsible for a certain mission. The first tier is aggregating data and forming it into decision matrices based on decision makers (DMs). The second tier is analyzing the criteria to obtain their weights by embracing Criteria Importance Through Intercriteria Correlation (CRITIC). The third tier recommends the optimal AuV based on Multi-Attributive Border Approximation Area Comparison (MABAC). All these tiers are implemented under the authority of Type 2 Neutrosophic Numbers (T2NNs) as branch of uncertainty theory. Hence, TTDM can treat mysterious circumstances and inaccurate information. © 2024 Computers and Industrial Engineering. All rights reserved.

关键词： Sustainable city

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Secure Blockchain Model for Iomt Smart Mobility System 3rd

Secure Blockchain Model for Iomt Smart Mobility System

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

作者： Farahat, Ibrahim Shawky Elhoseny, Mohamed Elmougy, Samir Abualkishik, Abedallah Zaid Aladrousy, Waleed Tolba, Ahmed Elsaid Faculty of Computers and Information Luxor University Luxor85951 Egypt Faculty of Computers and Information Mansoura University Mansoura35516 Egypt College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates American University in the Emirates Dubai United Arab Emirates

ISBN: (纸本)9789819726707

With technological advancements, electronic health records (EHRs) have replaced paper records to improve patient treatment quality and effectiveness in civil hospitals and military treatment facilities. These records must access by doctors and nurses to help in treating the patients through the Internet. Also, the enormous quantity of medical data for each patient makes it difficult to secure it. These reasons make it possible for medical data to be stolen by hackers. So, organizing and securing patients’ medical data in both civil and military hospitals is one of the main issues in the healthcare field. So, a new blockchain technique is proposed to secure the patient’s medical data. By making it decentralized, this proposed blockchain uses smart mobility technology. A smart Internet of Medical Things (IoMT) mobility system is used to collect patient vital signs and then store them in the blockchain. A modified version of the Nik-512 algorithm is utilized to connect the blocks in the blockchain. This modified version of the Nik-512 uses the LZ4 algorithm to compress data. This work aims to minimize the hash code computation time by preventing the hash code from beginning with zeros. Also, we build an authentication parity technique that checks the identification of the doctor and patient before the transaction happens. Also, in this work, a new master contract system is developed to guarantee transferring the money from the patient’s wallet to the doctor’s wallet after the doctor sends the treatment to the patient. The experimental results prove that the proposed work using Nik-512 is faster than all pervious compared blockchain versions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Metamaterial-Inspired Phased Array with High-Gain Beam-Steering for Future Cellular Networks

Metamaterial-Inspired Phased Array with High-Gain Beam-Steer...

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2024 IEEE International Symposium on Phased Array Systems and Technology, ARRAY 2024

作者： Basherlou, Haleh Jahanbakhsh Parchin, Naser Ojaroudi See, Chan Hwang Abd-Alhameed, Raed A. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom University of Bradford Faculty of Engineering and Informatics BradfordBD7 1DP United Kingdom

ISBN: (纸本)9798350392142

This paper investigates the features of a new metamaterial-inspired phased array that exhibits high-gain and steerable radiation capabilities, catering to the needs of future cellular networks. Its schematic incorporates eight modified dipole elements arranged in a 1 × 8 linear configuration. To enhance the radiation properties of the designed dipole resonators, 2 × 2 metamaterial unit cells are positioned alongside each dipole resonator. The dipole resonators are compact in size and employ microstrip-line feeds. The suggested smartphone phased array design achieves an impedance bandwidth of 28-32 GHz with -10 dB, offering over 80% total efficiencies and 13 dBi radiation gains. This promising performance aligns with the essential requirements of future cellular communications. © 2024 IEEE.

关键词： Antenna phased arrays

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Interval-Valued Fuzzy Bézier Surface Approximation 5

Interval-Valued Fuzzy Bézier Surface Approximation

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Sahrom, Nor Ashikin Emir Zulkifly, Mohammad Izat Idara Rosli, Siti Nur Universiti Teknologi Malaysia Faculty of Science Department of Mathematics Johor Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Negeri Sembilan Kampus Kuala Pilah Negeri Sembilan Malaysia

ISBN: (纸本)9798331528553

In the field of geometry and computer-aided design Bézier surfaces are well known for their flexibility and effectiveness, in depicting shapes. However, dealing with real-world data and model parameters often involves uncertainty and imprecision that regular Bézier surfaces struggle to handle. This piece introduces the idea of interval-valued fuzzy Bézier Surface Approximation, an approach that combines logic and interval arithmetic to tackle uncertainty in surface modeling. In this paper, interval-valued fuzzy Bézier surface approximation is introduced. The interval-valued fuzzy control net relation is defined and introduced. Next, the surface blending function is obtained by blending the interval-valued fuzzy control net with the Bernstein blending function. Lastly, the data points or interval-valued fuzzy control net relation of the basis function is illustrated using the approximation method with the interval-valued fuzzy concept and features to produce an interval-valued fuzzy Bézier surface. The application of Bézier surfaces can be extended to handle a wide range of complex modeling and offers a more precise and dependable tool for engineering, graphics, and data visualization applications. © 2024 IEEE.

关键词： Fuzzy control

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Modelling and Forecasting Mortality Rates Using a Neural Network-Integrated Lee-Carter Model 5

Modelling and Forecasting Mortality Rates Using a Neural Net...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Aityqah Yaacob, Nurul Mohamed, Ibrahim Noor Dina Ahmad, Siti College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Negeri Sembilan Negeri Sembilan Kuala Pilah Malaysia Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331528553

Artificialneural networks (ANNs), modelled after the brain's structure and function, can capture complex nonlinear relationships between predictors and response variables. This study integrates ANNs with the Lee-Carter (LC) framework using a multilayer feed-forward network to forecast the mortality index (kt), which tracks changes in mortality rates over time within the LC model. This mortality index is essential for forecasting future mortality patterns. Traditionally, the LC model uses an autoregressive integrated moving average (ARIMA) process to predict kt but ARIMA struggles with accurately forecasting future mortality trends. We compared the performance of the multilayer feed-forward network against ARIMA using mortality data from 19 countries, evaluating the models using root mean square error (RMSE) and mean absolute error (MAE). Our findings indicate that the multilayer feed-forward network outperforms ARIMA in forecasting mortality rates for 17 out of 19 countries. Additionally, integrating wavelet analysis and fuzzy logic with ANNs could further enhance forecasting accuracy by effectively managing non-stationary data, uncertainty, and complex patterns. © 2024 IEEE.

关键词： Prediction models

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Multivariate Bitcoin price prediction based on LSTM tuned by hybrid reptile search algorithm 16

Multivariate Bitcoin price prediction based on LSTM tuned by...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Todorovic, Mihailo Petrovic, Aleksandar Toskovic, Ana Zivkovic, Miodrag Jovanovic, Luka Bacanin, Nebojsa Singidunum University Faculty of Business Belgrade11000 Serbia Singidunum University Faculty of Informatics and Computing Belgrade11000 Serbia University of Pristina Teacher Education Faculty Kosovska Mitrovica38220 Serbia Singidunum University Faculty of Technical Sciences Belgrade11000 Serbia

ISBN: (纸本)9798350347029

This study focuses on analyzing historical data to forecast future trends in Bitcoin prices due to its influence on the business landscape. Its high volatility attracted attention to understanding the influencing factors for its price. This paper presents an empirical investigation using time-series data of various exogenous and endogenous variables. Closing prices of Bitcoin and Ethereum, along with the daily volume of Bitcoin-related tweets are examined for Bitcoin closing price prediction by a long-short term memory (LSTM) network, fine-Tuned by a hybrid adaptive reptile search algorithm. The analysis covers a three-year period, in which data is divided into training, validation, and testing sets. Comparative analysis against LSTM networks tuned by other high-performing metaheuristic algorithms demonstrates that the novel approach outperforms competitors in terms of standard regression metrics. © 2023 IEEE.

关键词： Long short-term memory

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5th International Conference on Robotic Automation System 2021, ICORAS 2021

作者： Khamis, Norazlina Rosli, Sarah Nafisa Tahir, Asni Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah. Kota Kinabalu88400 Malaysia

Kota Kinabalu (KK) is one of the pilot cities to implement smart city infrastructure in Malaysia. Given this privilege, lots of apps are needed to enable the city folks taking advantage of the highspeed bandwidth infrastructure. One of it is moving towards smart environment and smart government through supporting the green environment and cashless payment system for parking purposes. This paper describes the effort on the development of an apps, JumPark Bah! as an alternative to the current coupon-based parking system handled by the local authorities, Kota Kinabalu City Hall (KKCH). The current approach is quite cumbersome for KK city folks as they need to have a valid parking coupon displayed on the dashboard to park their car around KK city area. They must be able to correctly estimate the parking time or else summon will be issued if the parking time is exceeded. As for the KKCH, they need to assign many parking attendants for checking and validating the parking session. Thus, JumPark Bah! has been proposed as an alternative solution for KKCH by utilizing the concept of cashless payment parking system and is able to minimize the above-mentioned issues. The apps consist of a mobile application (for citizen and parking attendant) and the web-based system (for admin, i.e., KKCH). The web-based system allows the authority to monitor the fees, generate a report and even updates the user through announcement and news functionality. Based on the user evaluation using System Usability Scale (SUS), it has been found that JumPark Bah! has achieved 71.06% score which exceed the 68% score for good usability target. © Published under licence by IOP Publishing Ltd.

关键词： Smart city

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Modified Metaheuristics Optimization for Cyberbullying Detection on Online Data Science Platform 26

Modified Metaheuristics Optimization for Cyberbullying Detec...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Bacanin, Nebojsa Jovanovic, Luka Bujisic, Ilja Uzelac Stoean, Catalin Kaljevic, Jelena Cadjenovic, Jelena Antonijevic, Milos Zivkovic, Miodrag Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty for Health and Business Studies Belgrade Serbia Singidunum University Department of Business Economics Belgrade Serbia

ISBN: (纸本)9798331532833

Online harassment detection faces significant challenges due to its expansive reach and anonymity. Addressing this issue demands effective detection mechanisms capable of processing vast data streams and adapting to evolving online languages. Leveraging advancements in artificial intelligence, we propose a novel approach grounded in natural language processing (NLP) and metaheuristic algorithms. Our methodology integrates term frequency-inverse document frequency (TF-IDF) encoding and the AdaBoost algorithm for classification. To tackle the NP-hard problem of hyperparameter selection, we introduce a modified crayfish optimization algorithm (COA), termed GI-COA. This paper represents a pioneering effort in utilizing metaheuristic algorithms for hyperparameter selection in harassment detection models. Through experimentation, we demonstrate the efficacy of our approach in fostering a safer online environment. The best-performing optimized models demonstrate an accuracy exceeding 77%. © 2024 IEEE.

关键词： Data streams

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Neuro-Mimetic Developmental Architecture for Continual Learning through Self-Organizing Multimodal Perception Coordination

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IEEE Access 2025年 13卷 91625-91641页

作者： Dawood, Farhan Masuyama, Naoki Loo, Chu Kiong Iqra University Department of Computing & Technology Islamabad Pakistan Osaka Metropolitan University Graduate School of Informatics 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan University of Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia

Developing cognitive capabilities in autonomous agents stands as a challenging yet pivotal task. In order to construct cohesive representations and extract meaningful insights from the environment, the brain utilizes patterns from a variety of sensory inputs, including vision and sound. Furthermore, related sensory inputs can activate each other’s representations, highlighting the brain’s ability to associate and integrate information across different sensory channels – key to cognitive development and adaptive learning. Current learning methods often mirror the developmental process of infants, who enhance their cognition through guidance and exploration. These methods often struggle with issues such as catastrophic forgetting and stability-plasticity trade-offs. This study presents a novel brain-inspired hierarchical autonomous framework, Cognitive Deep Self-Organizing Neural Network (CDSN), designed to enable autonomous agents to acquire object concepts dynamically. The architecture includes dual parallel audio-visual information pathways, incorporating three layers based on a Topological Kernel CIM-based Adaptive Resonance Neural Network (TC-ART). The first layer, referred to as the receptive layer, learns and organizes visual attributes and object names autonomously in an unsupervised manner. Subsequently, the second layer, the concept layer, distills clustered results from the corresponding receptive layer to create succinct symbol representation. In a synchronized manner, visual and auditory concepts are combined concurrently in the third layer, the associative layer, to establish real-time associative connections between the modalities. Furthermore, this layer introduces a top-down response approach, allowing agents to independently retrieve associated modalities and adapt acquired knowledge in a hierarchical manner. Experimental evaluations conducted on object datasets demonstrate the proposed architecture’s efficacy in online learning and the association o

关键词： Multilayer neural networks

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