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14th IEEE Control and System Graduate Research Colloquium, ICSGRC 2023

作者： Sabri, Nurbaity Zulkifli, Zuhri Arafah Shari, Anis Amilah Halim, Faiqah Hafidzah Zaini, Hazrati Ismail, Khairul Nurmazianna Universiti Teknologi Malaysia School of Computing Faculty of Engineering Johor Skudai81310 Malaysia Universiti Teknologi Mara College of Computing Informatics and Mathematics Campus Jasin Melaka Malaysia

ISBN: (纸本)9798350346237

Oil palm significantly contributes to Malaysia's Gross Domestic Product (GDP). To maintain palm oil productivity, keeping the oil palm crop in good health, and free from nutrient deficiencies is necessary. Therefore, a method to analyze nutrient deficiencies is required. Nutrient deficiencies can be detected in the oil palm leaves. The study proposes an image-processing method that classifies three crucial nutrient deficiencies in a palm oil leaf: Nitrogen, Potassium, and Magnesium. To improve the processing rate, a convolution neural network (CNN) based on the AlexNet architecture has been implemented, which has been successful in image classification tasks and produced remarkable results in automated classification. The AlexNet architecture consists of five (5) convolved layers and three (3) fully connected layers, achieving an accuracy of 90.85%. This shows that AlexNet is an efficient algorithm for classifying nutrient deficiencies in palm oil leaves. However, a lower classification rate was observed for Magnesium compared to Nitrogen and Potassium. Due to the similarity in texture and color which may cause misinterpretation by the classifier. Further exploration of other CNN architectures will be conducted in the future to improve classification accuracy. © 2023 IEEE.

关键词： Palm oil

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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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Fully Homomorphic Encryption Accelerator Using DSP Embedded Multiplier 15

Fully Homomorphic Encryption Accelerator Using DSP Embedded ...

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15th International Conference on Computer Research and Development, ICCRD 2023

作者： Hashim, Shakirah Benaissa, Mohammed School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia University of Sheffield Department of Electronic and Electrical Engineering Sheffield United Kingdom

ISBN: (纸本)9781665487504

Encryption has an important role in cloud computing, as it secures the data into a ciphertext which is then protected with public key. But, how to encrypt massive cloud data? Do we have an encryption scheme that allow computation on the ciphertext, thus make the ciphertext dynamically encrypted? A recent breakthrough Fully Homomorphic Encryption (FHE) by Gentry is so much fulfilled the requirement of cloud encryption. However, it requires a huge key size, thus encryption process takes long and impractical for real-Time deployment. Hence, this work proposed FHE accelerator on hardware platform to speed up the encryption process so that practical encryption time could be achieved. We proposed an optimization on Digital Signal Processing (DSP) utilization on modern Field Programmable Gate Array (FPGA), Virtex7. Our proposed Karatsuba multiplier shows 15.94 times better Throughput vs Area than Comba Multiplier. With the significance performance, the proposed Karatsuba multiplier is then employed in the multiplication building block for FHE encryption process. A nearly practical encryption time is achieved between 0.005 s to 39.08 s for Toys to Large key size. In addition, our proposed multiplier has significantly small DSP usage, from 48 to 192 slices for Toys to Large key size. © 2023 IEEE.

关键词： Digital signal processing

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NAVIGATING THE IMPACT OF HUGE ELECTRICAL DEMAND ON GRID THROUGH INTELLIGENTLY CONTROLLED RENEWABLES AND EMERGING V2G TECHNOLOGY 14

NAVIGATING THE IMPACT OF HUGE ELECTRICAL DEMAND ON GRID THRO...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Tariq, Raheel Ali, Zunaib Saleem, Komal Christofides, Nicholas School of Engineering London South bank University London United Kingdom School of Architecture Computing and Engineering University of East London London United Kingdom Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Frederick Research Center Nicosia Cyprus

ISBN: (纸本)9781837242689

Among the biggest initiatives to help the UK reach its Net Zero goal are the conversion of the power grid to renewable energy sources (RESs) and the switch to all-electric vehicles (EVs) by 2050. However, because of the advent of dispersed unconventional energy sources and electric vehicles (EVs), the full switch to renewable energy sources and EVs poses a possible risk to power system stability, power quality, and energy management. To intelligently regulate the flow of energy and enhance the current power system, it is crucial to employ appropriate energy management strategies. This paper aims to establish a microgrid that is grid-connected and utilises multiple energy sources, including solar, battery energy storage, and electric vehicles. The goal is to create an intelligent algorithm that efficiently controls these resources and mainly enable bi-directional EV operation for vehicle-to-grid (V2G) application, resulting in both financial and environmental advantages. The main objective is to maximise the microgrid's overall profitability while minimising carbon emissions and effectively managing energy demand and network stability. It has been shown that utilising EVs as storage units via beneficial V2G technology not only stabilises renewable energy generation but also provides economic advantages. © The Institution of engineering & Technology 2024.

关键词： Microgrids

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Crop Yield Forecasting Based on Echo State Network Tuned by Crayfish Optimization Algorithm 2

Crop Yield Forecasting Based on Echo State Network Tuned by ...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Bacanin, Nebojsa Jovanovic, Luka Djordjevic, Marko Petrovic, Aleksandar Zivkovic, Tamara Zivkovic, Miodrag Antonijevic, Milos Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (纸本)9798350383652

The agriculture sector, once reliant only on external inputs for increased yields, faces repercussions of diminished natural systems and significant environmental impact. High-precision agriculture aims to curb excessive usage of fertilizers and pesticides while prioritizing sustainability. Modern technologies can shape decisions toward the betterment of soil health, plant protection, and yield enhancement. Predictive models employing machine learning aid farmers in crop performance estimation, disease detection, soil health assessment, and yield prediction among others. These insights inform field operations, logistics, and contract negotiations. Leveraging machinery data, weather data, and drone imagery using machine learning can help us refine farming practices. Recurrent neural networks (RNN) can aid in various farming challenges like plant stress detection and irrigation. Yet, challenges persist in optimizing hyperparameters, a task we tackled using the nature-inspired crayfish optimization algorithm (COA) adapted to tune echo state networks (ESN) for multivariate time series forecasting of crop yield. This study demonstrates the utility of machine learning in agriculture, enabling yield assessment for improved sustainability. © 2024 IEEE.

关键词： Recurrent 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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Viola–Jones Method for Robot Vision Purpose: A Software Technical Review 2nd

Viola–Jones Method for Robot Vision Purpose: A Software Tec...

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2nd International Congress of Electrical and Computer engineering, ICECENG 2023

作者： Khong, Wei Leong Moung, Ervin Gubin Chong, Chee Siang School of Engineering Monash University Malaysia Selangor Bandar Sunway Malaysia Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9783031527593

Viola–Jones (VJ) algorithm holds a prominent and undeniable position in the computer vision field. Despite not being the most recent technology, it aids research to achieve advanced levels. Researchers can practically implement the built-in detector to establish various esteemed inventions. The well-known image processing software MATLAB and iOS also offer the VJ-based functions, ‘***’ and ‘CIDetector’ in their platforms. However, it may be difficult for researchers outside the machine-learning field to choose suitable detectors. Therefore, this study intends to present the process for creating the detectors and explore the fundamental concept of a VJ detector. The software technical performances of these methods in processing 12 images with 155 faces are reviewed. This study found that the self-trained VJ detector has the most reliable accuracy since it achieved a 0.994 detection rate and precision of 1. Yet, it used the longest execution time and only be the best option if the execution time was not a significant factor. Overall, the CIDetector has the best performance in computation efficiency. It has a 0.776 detection rate and 0.833 precision. Nevertheless, it might be unable to detect small faces or faces with occluded eyes. If the user does not want any faces to be undetected, the ‘***’ is a better option as it has a 0.999 detection rate and 0.815 precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： MATLAB

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for engineering Problems

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Journal of Bionic engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms engineering optimization Spherical evolution algorithm Global optimization

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ZaRa-IU-NLP at EXIST 2023 - Sexism Identification: Specialized or Generalized? 24

ZaRa-IU-NLP at EXIST 2023 - Sexism Identification: Specializ...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Leech, Zackary Regulagedda, Ravi Kübler, Sandra Department of Linguistics Indiana University BloomingtonIN United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

We present our approach to EXIST (sEXism Identification in Social neTworks) Task 1, at CLEF 2023, comprising automatic sexism detection in both English and Spanish on Twitter data. We compare two methods, the first being a bi-ensemble method that combines two pre-trained BERT architectures, BETO and RoBERTa, for each specific language, Spanish and English, respectively. The second method utilizes the larger multilingual transformer, RoBERTa-XLM-base, and considers the entire dataset despite language differences. We show that the language-specific ensemble performs better than the generalized model and is a better choice when looking at sexism detection in mixed Spanish and English data. © 2023 Copyright for this paper by its authors.

关键词： Linguistics

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Accurate and Energy-Efficient Stochastic computing with Van der Corput Sequences 18

Accurate and Energy-Efficient Stochastic Computing with Van ...

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18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023

作者： Shoushtari Moghadam, Mehran Aygun, Sercan Riahi Alam, Mohsen Schmidt, Jonas I Najafi, M. Hassan Taherinejad, Nima School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA United States Institute of Computer Engineering Heidelberg University Heidelberg Germany

ISBN: (纸本)9798400703256

In stochastic computing (SC), data is represented using random bit-streams. The efficiency and accuracy of SC systems rely heavily on the stochastic number generator (SNG), which converts data from binary to stochastic bit-streams. While previous research has shown the benefits of using low-discrepancy (LD) sequences like Sobol and Halton in the SNG, the potential of other well-known random sequences remains unexplored. This study investigates new random sequences for potential use in SC. We find that Van Der Corput (VDC) sequences hold promise as a random number generator for accurate and energy-efficient SC, exhibiting intriguing correlation properties. Our evaluation of VDC-based bit-streams includes basic SC operations (multiplication and addition) and image processing tasks like image scaling. Our experimental results demonstrate high accuracy, reduced hardware cost, and lower energy consumption compared to state-of-the-art methods. © 2023 ACM.

关键词： Image processing

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