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International Journal of Interactive Mobile Technologies 2023年第15期17卷 120-134页

作者： Hassan, Ghassan Muslim Gumaei, Abdu Alanazi, Abed Alzanin, Samah M. Computer Science Department College of Science Mustansiriyah University Baghdad Iraq Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia

Network intrusion detection system (NIDS) has become a vital tool to protect information and detect attacks in computer networks. The performance of NIDSs can be evaluated by the number of detected attacks and false alarm rates. Machine learning (ML) methods are commonly used for developing intrusion detection systems and combating the rapid evolution in the pattern of attacks. Although there are several methods proposed in the state-of-the-art, the development of the most effective method is still of research interest and needs to be developed. In this paper, we develop an optimized approach using an extreme gradient boosting (XGB) classifier with correlation-based feature selection for accurate intrusion detection systems. We adopt the XGB classifier in the proposed approach because it can bring down both variance and bias and has several advantages such as parallelization, regularization, sparsity awareness hardware optimization, and tree pruning. The XGB uses the max-depth parameter as a specified criterion to prune the trees and improve the performance significantly. The proposed approach selects the best value of the max-depth parameter through an exhaustive search optimization algorithm. We evaluate the approach on the UNSW-NB15 dataset that imitates the modern-day attacks of network traffic. The experimental results show the ability of the proposed approach to classifying the type of attacks and normal traffic with high accuracy results compared with the current state-of-the-art work on the same dataset with the same partitioning ratio of the test set. © 2023 by the authors of this article. Published under CC-BY.

关键词： Classification (of information)

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Fabrication of LCE Microactuator Arrays Through Soft Lithography With Surface Alignment

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Journal of Microelectromechanical Systems 2025年第3期34卷 241-243页

作者： Li, Ke Kim, Jae-Jun Fernandes, Jayer Jiang, Hongrui University of Wisconsin–Madison Department of Electrical and Computer Engineering MadisonWI53705 United States University of Wisconsin–Madison Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Materials Science and Engineering Department of Ophthalmology and Visual Sciences McPherson Eye Research Institute MadisonWI53706 United States

Liquid crystal elastomers (LCEs) offer potentially programmable actuation through precise molecular alignment, making them ideal for microactuators in soft robotics and optical systems. However, achieving precise microscale alignment for LCE actuator arrays through scalable microfabrication approaches has been challenging. This letter introduces a low-cost surface alignment method to fabricate LCE microactuator arrays, reducing the dependency on expensive equipment, improving accessibility and manufactuarability compared to existing studies using field-assisted alignment methods. Thermal actuation tests demonstrated strong thermal responsiveness and stability. Our method offers a superior approach to integrating LCE microactuator arrays with modern microfabrication processes, promising multitudinous applications in MEMS and beyond. © 2025 IEEE. All rights reserved,.

关键词： Microfabrication

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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computer Modeling in engineering & sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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Reinforcement Learning based Adaptive Healthcare Decision Support Systems using Time Series Forecasting 5

Reinforcement Learning based Adaptive Healthcare Decision Su...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkadesan, R. Absin, S.J. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Healthcare decision-making is a complex and crucial process that must take into account changing patient situations and medical trends. It is frequently difficult for conventional decision support systems to adjust to these shifting circumstances. In this work, we present a novel method for creating adaptive healthcare decision support systems by combining time series forecasting and reinforcement learning (RL). The main goal is to create a system that can use reinforcement learning (RL) to develop optimal policies for making decisions by utilizing time series data of patients from the past. Our system uses RL in conjunction with time series forecasting methods like LSTM (Long Short-Term Memory) networks and ARIMA (Auto Regressive Integrated Moving Average) to estimate future patient outcomes and healthcare resource requirements. There are several different components in the system architecture. The time series forecasting models are trained using historical healthcare data, which includes patient demographics, medical histories, and treatment outcomes. Forecasts for important healthcare parameters like patient recovery rates, disease progression, and resource consumption are produced by these models. According to the anticipated results, RL algorithms are used to learn decision policies. While interacting with a simulated environment that represents patient care scenarios, the RL agent aims to maximize long-term rewards, which may include reduced expenses, better patient outcomes, or more efficient use of available resources. Using actual healthcare datasets, the adaptability and efficacy of suggested approach will be assessed. The efficacy of RL-based decision policies, accuracy of projected results, and the system's adaptability to shifting healthcare dynamics are all examples of performance measures. © 2024 IEEE.

关键词： Decision support systems Adaptive systems Accuracy Time series analysis Systems architecture Medical services Reinforcement learning Predictive models Forecasting Long short term memory

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IoT-Based Indoor Air Quality Management System for Intelligent Education Environments

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IEEE Internet of Things Journal 2025年第11期12卷 18031-18041页

作者： Rosa-Bilbao, Jesus Butt, Fatima Sajid Merkl, David Wagner, Matthias F. Schafer, Jorg Boubeta-Puig, Juan University of Cadiz Department of Computer Science and Engineering Cádiz11519 Spain University of Cadiz School of Engineering Cádiz11519 Spain Frankfurt University of Applied Sciences FB2 Computer Science and Engineering Frankfurt60318 Germany

One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this article, we propose an efficient indoor air quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity. The aim is to create an intelligent environment for IAQ. The data is captured and monitored using an Internet of Things network of sensors, manufactured by ourselves, in different lecture rooms at the university. The obtained data is then processed and correlated in real time using a complex event processing engine and analyzed by machine/deep learning algorithms. A long short-term memory neural network is proposed to forecast IAQ. Then a decision tree regressor is used to identify the relationships between temperature, humidity and different pollutants like CO2 and PM2.5. © 2014 IEEE.

关键词： Indoor air pollution

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Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques 2nd

Photovoltaics Cell Anomaly Detection Using Deep Learning Tec...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Al-Dulaimi, Abdullah Ahmed Hameed, Alaa Ali Guneser, Muhammet Tahir Jamil, Akhtar Department of Electrical and Electronics Engineering Karabuk University Karabuk Turkey Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9783031509193

Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning Detection Electroluminescence image detection Solar panel

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

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Energy and AI 2024年第2期16卷 385-395页

作者： Amir Miraki Pekka Parviainen Reza Arghandeh Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway Department of Informatics University of BergenBergenNorway

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Causal inference Electricity demand forecasting Explainable artificial intelligence(XAI) Graph neural network

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A Comprehensive Review of Block Chain Based Multi Factor Authentication 1st

A Comprehensive Review of Block Chain Based Multi Factor Aut...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Alazzawi, Esraa Mohammad Jamil, Akhtar Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031823763

The ease and high availability of public and personal data in the existing automated and digital environments have made our technological systems vulnerable to security threats. The conventional approach of passwords and usernames is hence insufficient to ensure the security of data from cyber threats. Multi Factor Authentication can thus ensure effective security thereby minimizing the challenges of Single factor authentication. Blockchain based multi factor authentication (MFA) involves using blockchain for authentication purpose to enhance the security of a system. This paper reviews the recent advancements in the field of multi-factor authentication with blockchain as a means. It analyses the effectiveness of Blockchain based MFA systems by evaluating their strengths and weaknesses. It further studies the gaps in the recent literatures and summarises the future scope for Blockchain and MFA. It discusses techniques such as lightweight cryptographic methods, Arbiter PUF (Public Unclonable function), Clustering, Smart contracts and Machine learning used for authentication purposes. These techniques can be extended to include behavioural factors in multi-factor authentication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Authentication Protocol

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Improved Belief Propagation Decoder for LDPC-CRC-Polar Codes with Bit-Freezing

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China Communications 2024年第7期21卷 135-148页

作者： Qasim Jan Yin Chao Pan Zhiwen Muhammad Furqan Zakir Ali You Xiaohu National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211100China Department of Computer Science National University of Computer and Emerging Sciences Peshawar Campus 25100Pakistan Department of Software Engineering Capital University of Science and Technology(CUST)Islamabad 44000Pakistan Department of Computer Sciences Bahria University Lahore CampusPakistan

Though belief propagation bit-flip(BPBF)decoding improves the error correction performance of polar codes,it uses the exhaustive flips method to achieve the error correction performance of CA-SCL decoding,thus resulting in high decoding complexity and *** alleviate this issue,we incorporate the LDPC-CRC-Polar coding scheme with BPBF and propose an improved belief propagation decoder for LDPC-CRC-Polar codes with bit-freezing(LDPCCRC-Polar codes BPBFz).The proposed LDPCCRC-Polar codes BPBFz employs the LDPC code to ensure the reliability of the flipping set,i.e.,critical set(CS),and dynamically update *** modified CS is further utilized for the identification of error-prone *** proposed LDPC-CRC-Polar codes BPBFz obtains remarkable error correction performance and is comparable to that of the CA-SCL(L=16)decoder under medium-to-high signal-to-noise ratio(SNR)*** gains up to 1.2dB and 0.9dB at a fixed BLER=10-4compared with BP and BPBF(CS-1),*** addition,the proposed LDPC-CRC-Polar codes BPBFz has lower decoding latency compared with CA-SCL and BPBF,i.e.,it is 15 times faster than CA-SCL(L=16)at high SNR regions.

关键词： belief propagation bit-flipping concatenated codes LDPC-CRC-Polar codes polar codes

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