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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Livada, Časlav Hodak, David Department of Software Engineering Faculty of Electrical Engineering Computing and Information Osijek Croatia Faculty of Electrical Engineering Computing and Information Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

The goal of this paper is to demonstrate the power of machine learning and deep learning combined with powerful tools used in creating virtual simulations. This is made possible by the Unity Game Engine, a software used for the development of 2D and 3D video games and simulations, and the ML-agents plugin, which contains algorithms for training intelligent agents. The basic principles and algorithms of Deep Learning are described. Agent uses these methods to perceive the environment and solve certain tasks. In this paper, a simulation is created in which the agent tries to find hidden objects using machine learning. When the agent finds an item, a new location for the item is randomly generated. It must also complete the task in a specified amount of time. With the right implementation and parameters, the agent could successfully complete the task. © 2022 IEEE.

关键词： Reinforcement learning

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Preference Definition in a Multi-agent System Approach for User-Centric Internet of Things Smart Environments 18th

Preference Definition in a Multi-agent System Approach for...

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18th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Mandaric, Katarina Jezic, Gordan Faculty of Electrical Engineering and Computing Internet of Things Laboratory University of Zagreb Unska 3 HR Zagreb10000 Croatia

ISBN: (纸本)9789819764686

The term Ambient Intelligence (AmI) has been a topic of interest for many years, as it has the potential to transform services and environments—from living and working spaces to hospitals. Context awareness is a prerequisite for the development of truly intelligent services, i.e. it is necessary to detect and understand the state of the space as well as events/activities taking place. Additionally, for the complete adaptation of the space to the user, it is necessary to take the user’s preferences into account. Defining user preferences is a very complex area with numerous approaches, starting with the development of simple automated services in a smart home, where the user is offered control of a simple set of rules to define their wants and needs. The paper conducts an analysis of preference definitions in different environments and proposes a preference definition for the use case of smart lighting and smart heating and cooling system in a Multi-Agent System approach. This proposed definition forms the basis for effective negotiation between users with different preferences, where conflicts are inevitable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

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A Novel Cyber Threat Intelligence (CTI) Framework for Detecting Android Malware: Work on Progress

A Novel Cyber Threat Intelligence (CTI) Framework for Detect...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Wijaya, Andri Stiawan, Deris Abdiansah Suprapto, Bhakti Yudho Saputra, Iwan Pahendra Anto Rini, Dian Palupi Sayfudin Idris, Mohd.Yazid Budiarto, Rahmat Universitas Katolik Musi Charitas Faculty of Engineering Universitas Sriwijaya Department of Information System Indonesia Universitas Sriwijaya Faculty of Computer Science Indonesia Sriwijaya University Faculty of Engineering Electrical Engineering Dept. Palembang Indonesia Sriwijaya University Faculty of Comp.Science Informatic Engineering Dept. Palembang Indonesia Universitas Muhammadiyah Palembang Indonesia Universiti Teknologi Malaysia School of Computing Faculty of Computing Johor81310 Malaysia Al-Baha University College of Computing and Information Al Aqiq Saudi Arabia

ISBN: (纸本)9798331511470

The latest approach in cyber threat intelligence (CTI) is becoming increasingly crucial in detecting evolving cyber threats, including malware on Android devices. This problem is getting deeper when the number of Android users in Indonesia is increasing compared to other platforms. Therefore, it is necessary to develop a new method that integrates the CTI framework with the use of machine learning to improve the accuracy and speed in detecting Android malware. By utilizing intelligence data from structured threat reports, this method aims to identify suspicious patterns and abnormal behavior that may not be detected by conventional security systems so that with this updated security system it is proactive towards the development of new types of malware threats, with the hope of being able to make a significant contribution to strengthening especially mobile devices with the Android platform and being able to provide more effective solutions in reducing the risk of malware attacks on Android. © 2024 IEEE.

关键词： Cyber attacks

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Implementation of Power Meter System Security in Smart Grids with QRNG Method 5

Implementation of Power Meter System Security in Smart Grids...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Patel, Aum Shrivastava, Simran Sujatha, G. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350383522

Smart grids face emerging threats as connectivity expands attack surfaces. To strengthen smart meter data encryption through optimized integration of a cloud-based quantum random number generator (QRNG) service. QRNGs leverage intrinsic quantum properties to produce true randomness for cryptographic keys. An architecture is presented utilizing a QRNG service on the IBM quantum computing platform. Quantum entropy is remotely sampled and delivered on-demand via APIs to generate quantum random AES encryption keys for smart meter communications. Homomorphic encryption secures credential transmission. Compared to mathematical pseudorandom number generators, the quantum origin provides stronger security against brute force attacks and future quantum computing decryption. Tailoring QRNG generation and integration focuses on the performance, cost, and deployment constraints of grid-wide encryption hardening. The optimized approach aims to maximize randomness while minimizing complexity. By hardening cryptography via readily available quantum key generation, the QRNG integration delivers indispensable security enhancement. © 2024 IEEE.

关键词： Cryptography cryptography protocols cybersecurity encryption privacy QKD QRNG quantum entropy quantum key distribution quantum random number generator quantum uncertainty security smart grid smart meter true randomness

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Optimizing Foreign Exchange Trading Performance Through Reinforcement Machine Learning Framework 14

Optimizing Foreign Exchange Trading Performance Through Rein...

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14th International Conference on Computer and Knowledge engineering, ICCKE 2024

作者： Moung, Ervin Gubin Sufian, Maisarah Mohd Farzamnia, Ali Murnizam, Hani Yasmin binti Liaw, Valentino Angeline, Lorita Faculty of Computing and Informatics University Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom Faculty of Engineering Universiti Malaysia Sabah Sabah Malaysia

ISBN: (纸本)9798331511272

The ever-changing financial market of foreign exchange attracts many traders. Traders must make wise decisions to avoid significant losses when buying and selling currencies. This project intends to reduce the chance of suffering from loss by providing a trading strategy. The research on developing a trading strategy specifically for the foreign exchange market is still lacking due to the limitation in selecting the best model to create a trading strategy, which is still a working area. Even with current research on trading strategy, it tends not to work overtime due to unpredictable market trends. Therefore, this paper proposed three models using the algorithms A2C, PPO & DQN to find the best strategy in foreign exchange trading, analyze the impact of individual features on the trading strategy and identify the most influential features to develop the best trading strategy using reinforcement learning and finally evaluate the performance on unseen data using Sharpe Ratio, Sortino Ratio, Omega Ratio, Profit & Loss (%), Maximum Drawdown (%) and Cumulative Score. The experiment result showed that the PPO algorithm performed best on 2 of the currency pairs which is GBP/USD and USD/JPY, with a Sharpe Ratio of 0.23 and 0.70, respectively, and a Profit & Loss of 7.4% and 16.78%, respectively, when tested on unseen data. Meanwhile, when tested on unseen data, the A2C model performed the best on the EUR/USD currency pair with a Sharpe Ratio of 0.16 and a Profit & Loss of 3.34%. ©2024 IEEE.

关键词： Reinforcement learning

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

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computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Technology Management Stony Brook University NY11794 United States Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation. © The Author(s) 2025.

关键词： Power management (telecommunication)

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computing of LQR Technique for Nonlinear System Using Local Approximation

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Computer Systems Science & engineering 2023年第7期46卷 853-871页

作者： Aamir Shahzad Ali Altalbe Mechanical Engineering Department The University of LahoreLahorePakistan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah80258Saudi Arabia

The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local *** LQR is an excellent method for developing a controller for nonlinear *** provides optimal feedback to make the closed-loop system robust and stable,rejecting external ***-based optimal controller for a nonlinear system such as a rotatory inverted pendulum has not been designed and implemented using Newton-Euler,Lagrange method,and local ***,implementing LQR to an underactuated nonlinear system was vital to design a stable controller.A mathematical model has been developed for the controller design by utilizing the Newton-Euler,Lagrange *** nonlinear model has been linearized around an equilibrium *** and nonlinear models have been compared to find the range in which linear and nonlinear models’behaviour is *** LQR function and system dynamics have been used to estimate the controller *** the performance evaluation of the designed controller,Simulink has been *** and nonlinear models have been simulated along with the designed *** have been performed for the designed controller over the linear and nonlinear system under different conditions through varying system *** results show that the system is stable and robust enough to act against external *** controller maintains the rotary inverted pendulum in an upright position and rejects disruptions like falling under gravitational force or any external disturbance by adjusting the rotation of the horizontal link in both linear and nonlinear environments in a specific *** controller has been practically designed and *** is vivid from the results that the controller is robust enough to reject the disturbances in milliseconds and keeps the pendulum

关键词： computing rotary inverted pendulum(RIP) modeling and simulation linear quadratic regulator(LQR) nonlinear system

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Deep Learning Based Computer Vision Bound Fall Detection for Elderly People 21

Deep Learning Based Computer Vision Bound Fall Detection for...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Abdoul-Halim, Mohamed Abdallah Vengusamy, Sivakumar Asia Pacific University of Technology and Innovation Faculty of Computing Engineering and Technology School of Computing Science and Software Kuala Lumpur Malaysia

ISBN: (纸本)9798350318821

Due to the devastating effects, such as increased mortality and morbidity, falls among the elderly are a significant health issue globally. Supporting the elderly in their daily activities is a significant problem for society because they are the age group with the largest rate of population growth. An automated fall detection system has gained popularity in the healthcare sector due to the social and financial benefits it offers. Vision-based fall detection systems have a great promise now that Smart Homes are a growing trend and there are more cameras in our everyday environments. An autonomous real-time fall detection system based on vision is given in this paper. In this research, we suggested a 3D RGB camera-based method for fall detection. Machine learning algorithms, which were trained on characteristics retrieved by a deep learning-based computer vision algorithm, are used to distinguish between occlusion, falling, and routine everyday activities. Deep learning algorithms are used to identify people in videos. Two datasets - one developed locally and the other made public - were used for the experimental validation of the suggested methodology. Several assessment metrics are computed for evaluation. This analysis demonstrated the efficacy of the suggested solution. Temporal characteristics in a video series are captured using motion history images, and spatial features are then effectively retrieved for classification using a depth-wise convolutional neural network. © 2023 IEEE.

关键词： Fall detection

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M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast Cancer Histopathology Using Contextual Patches and Attention Mechanism 22

M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast C...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Sufyan, Arbab Fauzi, Mohammad Faizal Ahmad Kuan, Wong Lai Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Computing & Informatics Cyberjaya Malaysia

ISBN: (纸本)9798331520526

Histopathological analysis, particularly of nuclear morphology, is critical for identifying malignancies. Accurate nuclei segmentation plays a pivotal role in this process, as it enables detailed assessment of nuclear size, shape, and distribution patterns. Traditional segmentation methods, however, often fail to capture fine details, lack broader context, and struggle with overlapping nuclei of varying sizes and shapes, especially when relying on single-scale approaches. To address these challenges, we propose a multi-scale context-based encoder-decoder model named M3-Net (Multi-Scale, Multi-Level, Multi-Stream Network) that integrates both global and local tissue features. Evaluations demonstrate that M3-Net effectively segments overlapping nuclei and diverse structures, providing a robust solution for automated nuclei segmentation in breast cancer pathology. © 2025 IEEE.

关键词： Breast Cancer Histopathology Multi-Scale Nuclei Segmentation

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