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computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ahmad Faizal Bin Tajuddin Mohammad Hafiz Mohd Yusof Mohammed Balfaqih Akram A. Almohammedi Sam Darshi Nadhem Qaid College of Computing Informatics and Mathematics Universiti Teknologi MARA UiTM Tapah Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Electrical and Electronic Engineering Department Karabük University Karabük Turkey Department of Electrical Engineering Indian Institute of Tehcnology Ropar Punjab India R&D Engineer Lead Marine Powertrain Controls & Electronics Caterpillar Inc Houstan Texas USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

This paper presents a comparative analysis of three widely used encryption algorithms, namely AES (Advanced Encryption Standard), RC (Rivest Cipher), and Twofish, with a focus on their performance in data encryption and decryption. The study aims to evaluate the strengths and weaknesses of these algorithms in terms of security, speed, and efficiency. The analysis involves benchmarking the algorithms against various performance metrics, including encryption time, decryption time, key size, and resistance against cryptanalysis attacks. Additionally, the research explores the impact of different data sizes and configurations on the performance of each algorithm. The findings of this study will provide valuable insights for decision-makers and practitioners in selecting the most suitable encryption algorithm for their specific needs, considering the tradeoffs between security and performance.

关键词： Resistance Ciphers Three-dimensional displays Data security Benchmark testing Encryption Timing Information technology Standards

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Design oligoporous-core based multimode fiber for mode division multiplexing applications

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Optical and Quantum Electronics 2025年第5期57卷 1-24页

作者： Mitu, Sumaiya Akhtar Abdulrazak, Lway Faisal Ibrahim, Sobhy M. Tahhan, Shaymaa R. Hossain, Md Bellal Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering University of Asia Pacific 74/A Green Rd Dhaka1205 Bangladesh Department of Information and Communication Technology Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Laser and Optoelectronics Engineering College of Engineering Al-Nahrain University Jadriya Baghdad64074 Iraq School of Electrical and Information Engineering The University of Sydney SydneyNSW Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

A polarization-maintaining oligoporous-core-based multi-mode fiber is proposed. By tuning the air hole, as well as the core number, shape, size, and position up to 28 distinct linearly polarized (LP) modes are obtained. The Finite Element Method (FEM) is used to perform the numerical investigations. In addition, various materials combinations are used as a doping with silica which is highly helpful to increase or decrease the refractive index of the core material. The multimode fiber is identified by the normalized frequency or V parameter. Besides, the high birefringence value, low loss value, minimum crosstalk with high sensitivity response of 1.46×10-2, 2×10-11dB/m, 41.80 dB and 88,280.46 nm/RIU, respectively, are achieved from the numerical investigations over the wavelength range from 1.55 µm to1.65 µm for the different LP modes. Moreover, good responses also obtain for the numerical aperture, V number, coupling length and other parameters. In the end, every value reveals both an easy-to-fabricate structure design as well as adequate performance analysis. The suggested fiber structure can support many modes and might be applicable in the field of optical communications and spatial multiplexing based on the user demand. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fiber to the x

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Regression Analysis of Humidity and Temperature on Chicken Lifespan

Regression Analysis of Humidity and Temperature on Chicken L...

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Agrosystem Engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Sittilin Salleh Razak Mohd Ali Lee Nurul Rawaida Ain Burhani Kennedy Juani Farah Hasanah Ab Jaid Electrical and Electronics Engineering Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Mechanical Engineering Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Veterinary Services Sabah Malaysia Computer Science (Data Science) Faculty of Computing & Informatics Universiti Malaysia Sabah

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

This study presents a comprehensive analysis of the effects of humidity and temperature variations on the lifespan of Ross broiler chickens at two different locations over two 35-day experimental periods. The study initially included a group of 20 chickens, followed by another group of 41 chickens. Through rigorous regression analysis, the research evaluates the correlation between environmental variables and chicken lifespan, using a multi-sensor system integrated with IoT technology to monitor temperature and humidity values in poultry farms. The results presented in significant MSE and R 2 values for Batch 2 using correlation matrix and polynomial regression provide valuable insights for optimizing poultry management practices.

关键词： Temperature sensors Temperature measurement Correlation Humidity Polynomials Regression analysis Monitoring

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Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer Detection

arXiv

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arXiv 2024年

作者： Galazis, Christoforos Wu, Huiyi Goryanin, Igor Department of Computing Imperial College London London United Kingdom National Heart & Lung Institute Imperial College London London United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom Okinawa Institute Science and Technology Okinawa Japan MMWR Ltd United Kingdom

Improving breast cancer detection and monitoring techniques is a critical objective in healthcare, driving the need for innovative imaging technologies and diagnostic approaches. This study introduces a novel multi-tiered self-contrastive model tailored for microwave radiometry (MWR) in breast cancer detection. Our approach incorporates three distinct models: Local-MWR (L-MWR), Regional-MWR (R-MWR), and Global-MWR (G-MWR), designed to analyze varying sub-regional comparisons within the breasts. These models are integrated through the Joint-MWR (J-MWR) network, which leverages self-contrastive results at each analytical level to improve diagnostic accuracy. Utilizing a dataset of 4,932 female patients, our research demonstrates the efficacy of our proposed models. Notably, the J-MWR model achieves a Matthew’s correlation coefficient of 0.74 ± 0.018, surpassing existing MWR neural networks and contrastive methods. These findings highlight the potential of self-contrastive learning techniques in improving the diagnostic accuracy and generalizability for MWR-based breast cancer detection. This advancement holds considerable promise for future investigations into enabling point-of-care testing. The source code is available at: https://***/cgalaz01/self_contrastive_ mwr. © 2024, CC BY.

关键词： Radiometry

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Multimodal Feature Extraction for Memes Sentiment Classification 2

Multimodal Feature Extraction for Memes Sentiment Classifica...

引用

2nd IEEE Conference on Information Technology and Data science, CITDS 2022

作者： Ouaari, Sofiane Tashu, Tsegaye Misikir Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022 IEEE.

关键词： computer vision

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xIDS-EnsembleGuard: An Explainable Ensemble Learning-based Intrusion Detection System

xIDS-EnsembleGuard: An Explainable Ensemble Learning-based I...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Muhammad Adil Mian Ahmad Jan Safayat Bin Hakim Houbing Herbert Song Zhanpeng Jin Deptartment of Computer Science University at Buffalo Buffalo USA College of Computing and Informatics University of Sharjah Sharjah UAE School of Information Systems University of Maryland Baltimore County USA School of Future Technology South China University of Technology Guangdong China And University at Buffalo Buffalo USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

关键词： Deep learning Analytical models Accuracy Intrusion detection Training data Predictive models NSL-KDD Reliability Ensemble learning Long short term memory

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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease

Informatics and Health

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informatics and Health 2024年第2期1卷 70-81页

作者： Temidayo Oluwatosin Omotehinwa David Opeoluwa Oyewola Ervin Gubin Moung Department of Mathematics and Computer Science Federal University of Health SciencesP.M.B.145OtukpoNigeria Department of Mathematics and Statistics Federal University KashereP.M.B.0182GombeNigeria Data Technologies and Applications(DaTA)Research Group Faculty of Computing and InformaticsUniversiti Malaysia SabahKota Kinabalu 88400SabahMalaysia

Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection *** diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient *** aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic ***:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this *** Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing ***-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the *** LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its *** optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and ***:The optimized LightGBM model showed significant improvement in early CHD *** baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of *** MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of *** combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust ***:The optimized model demonstrated outstanding performance in early CHD *** study’s strengths include its comprehensive approach to addres

关键词： Clinical decision making Coronary heart disease Light gradient-boosting machine Machine learning MICE Tree-structured Parzen estimator

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Tele-Collaboration System in CVLab

Informatica (Slovenia)

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Informatica (Slovenia) 2022年第2期46卷 223-233页

作者： Mechta, Djamila Harous, Saad Djoudi, Mahieddine Department of Computer Science LRSD Lab College of Sciences University of Ferhat ABBAS-Sétif-1 Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Laboratoire Techne-UR 20297 1 rue Raymond Cantel Bâtiment A3 TSA Poitiers Cedex 911102 86073 France

This work is part of a learning environment that has virtual laboratories that are designed for distant practical work (Tele-PW). In these environments, Tele-PW is performed in two modes: individual and/or collaborative. In this paper, we are concentrating on the tele-collaborative distant practical work model. The work, presented in this paper, proposes an artificial agent called Synchronizer Coordinator Agent (SCA) to synchronize and coordinate the activities of a cognitive process in order to build and maintain a shared conception of a distant practical work between a set of learners. This agent provides certain features such as managing groups of learners, coordinating tasks, shared workspace among members of the Working Group. It is also responsible for the synchronization of workspace agents when they want to manipulate shared virtual objects simultaneously. We have chosen Petri nets to illustrate the principle of granting access to shared objects in the case of simultaneous requests. Experimental results show the effectiveness, of the artificial agent within any tele-collaborative/tele-cooperative learning situation. Several situations describe the geographical/time dispersion of learners and tutors in our system are considered during the system design phase. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Petri nets

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Unmasking Digital Deceptions: A Comprehensive Survey of Synthetic Reality Analysis Across Multimedia Domains

Unmasking Digital Deceptions: A Comprehensive Survey of Synt...

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Electronic Systems and Intelligent computing (ICESIC), International Conference on

作者： Sneha Chaudhary Komal Chaurasiya Suthir Sriram Ravikumar S Nivethitha V Thangavel M Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology College of Information and Technology United Arab Emirates University

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The general accessibility of social networking sites like Facebook, Myspace and TikTok has transformed the way information is communicated, but it has also made it simpler to spread misleading information, thanks to cutting-edge technologies like deepfakes. The use of artificial intelligence in deepfake technology produces material that is remarkably life- like but fake, making it difficult to identify and remove fraudulent content. The evolution of deepfake technology is reviewed in this study, starting with early breakthroughs like the Video Rewrite Program and continuing with more current developments like the Synthesizing Obama and Face2Face projects. We provide a comprehensive analysis of the state-of-the-art detection strategies, namely Region-based CNNs (RCNNs), Conventional Neural Networks (CNNs), and hybrid approaches that combine various deep learning techniques. Though these methods have advanced, there are still issues with accuracy, integration complexity, and adaptability to new deepfake techniques. This paper suggests a novel approach that integrates image, video, and audio analysis into a single detection framework in order to overcome these difficulties. The new approach is directed towards improvement of detection accuracy and the lowering of the false alarm using advanced and deep learning engines along with the real time processing requirements. Early results indicate that the detection capabilities have significantly improved compared to existing solutions operating in real time environment while having less latency and higher recall and precision rates. This method is more effective for deepfake detection and at the same time helps in augmenting the comprehensiveness and efficiency of the system there by making it suitable for wider uses. The study emphasizes the presence of the need for the evolution of static approaches towards the effective defection of sophisticated false information and the justification of new possibilities in the fight a

关键词： Deep learning Surveys Deepfakes Technological innovation Accuracy Video on demand Social networking (online) Real-time systems Web sites Protection

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Virtual Staging Technologies for the Metaverse

Virtual Staging Technologies for the Metaverse

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Muhammad Tukur Yehia Boraey Sara Jashari Alberto Jaspe Villanueva Uzair Shah Mahmood Al-Zubaidi Giovanni Pintore Enrico Gobbetti Jens Schneider Marco Agus Noora Fetais College of Science and Engineering HBKU Doha Qatar Department of Computer Science Qatar University Doha Qatar VCCVIS High Perf. Visualization KAUST Jeddah Saudi Arabia Visual and Data-intensive Computing CRS4 Cagliari Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

We discuss virtual staging technologies, focusing on two primary pipelines for creating and exploring immersive indoor environments in the metavers: an AI-based image processing pipeline and a LIDAR-based pipeline. The AI-based image processing pipeline leverages advanced AI algorithms for tasks such as clutter removal, semantic style transfer, and super-resolution, enabling rapid generation of high-quality, photorealistic virtual environments from single panoramic images. The LIDAR-based pipeline captures measurable 3D models of indoor spaces, facilitating immersive editing and collaborative design through real-time interaction with high-fidelity virtual environments. A qualitative comparative analysis of these technologies highlights their strengths and limitations in various applications. The practical implications of these pipelines are discussed, particularly their potential to transform industries such as real estate, furniture retail, interior design, construction, remote collaboration, and immersive training. The paper concludes with suggestions for future research, including conducting user studies, integrating the two pipelines, and optimizing technologies for mobile and edge devices to enhance accessibility and usability.

关键词： Training Solid modeling Three-dimensional displays Image processing Pipelines Superresolution Semantics Collaboration Transforms Usability

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