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5th International Conference on Education Technology Management, ICETM 2022

作者： Yau, Peter ChunYu Kim, Yongmin Wong, Dennis Pang, Patrick Kan, Stanley Cao, Qi School of Computing Science University of Glasgow United Kingdom School of Engineering University of Glasgow United Kingdom Department of Computer Science State University of New York Korea Republic of Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781450398015

After two years of online learning, minor changes happened in some tiny places which we may not always notice. These changes may impact the school management, education arrangement, and curriculum design: what do students expect from their teachers, and the school? Are they expecting forever online learning, and getting their degree completely online? In this study, we examined 93 final-year students from the school of computing science which mainly used work-based learning applied to learn pedagogy. We analyze their viewpoint on how COVID-19 changed their understanding of teaching and learning: what is the role of teachers and students? And how do they see the arrangement for the internship program? We found that over 92% of students agree that a creditable degree can better secure a job;over 68% of the students believed that face-to-face teaching is still a better choice, but at the same time, over 73% wish to have the materials recorded so that they can access them at their convenience time. We also examined some of the course design elements, especially those related to the internship program, to achieve a possible curriculum improvement for the students in the Asia Pacific region. © 2022 ACM.

关键词： Students

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A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Husain, Saif O. Meghana, A. Soujanya, Rajireddy Sunil, G. Reddy Muvva, Vijaya Bhaskar The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman

ISBN: (纸本)9798350375442

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification. © 2024 IEEE.

关键词： Median filters

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Radhakrishnan, Venkateswaran Rodda, Vijayakumari Sowmiya, S. Sarker, Abdur Rahman Palanikumar, R. Asokan, R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Dhofar Oman Krishna University Department of Computer Science Rudravaram Machilipatnam521004 India Sri Eshwar College of Engineering Coimbatore641202 India Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India Kongunadu College of Engineering and Technology Department of Ece Tamil Nadu Trichy India

ISBN: (纸本)9798331521394

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security. © 2025 IEEE.

关键词： Convolutional neural networks

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Skills Extraction from Entities in Arabic CVs

Skills Extraction from Entities in Arabic CVs

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Mohamed Abdul Karim Sadiq Thirumurugan Shanmugam Nasser Mohammed Al-Fannah Firdous Kausar College of Computing and Information Science University of Technology and Applied Sciences Suhar Oman Department of Mathematics and Computer Science Fisk University Nashville Tennessee USA

Named Entity Recognition in low resource languages such as Arabic is relatively less accurate. Despite this, many applications attempt query-document matching, which necessitates entity resolution. We explore certain possible solutions in this regard to enhance the matching process. A system is proposed to build a sub graph of terms in the Knowledge Graph. These terms specifically deal with skills in the Arabic documents related to the Human Resources domain. The accuracy measures of skills extraction using two different Language Models viz., SparkNLP and Hatmi are presented. The results are promising and have scope for improvement in entity resolution.

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

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computers, Materials & Continua 2021年第12期69卷 2917-2932页

作者： Muhammad Rizwan Latif Muhamamd Attique Khan Muhammad Younus Javed Haris Masood Usman Tariq Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan Wah Engineering College University of WahWah Cantt47080Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton *** 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton *** 1.5 million people in Pakistan are engaged in the cotton value ***,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton *** diagnosis is not a good solution due to several factors such as high cost and unavailability of an ***,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early *** this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature *** proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further *** final recognition,a Cubic SVM approach was utilized and validated on a prepared *** the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..

关键词： Plants diseases data augmentation deep learning features fusion features selection

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EFFICIENT NONLINEAR MULTISCALE SPECTRAL GFEM applied TO COMPOSITE AEROSPACE STRUCTURES 23

EFFICIENT NONLINEAR MULTISCALE SPECTRAL GFEM APPLIED TO COMP...

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23rd International Conference on Composite Materials, ICCM 2023

作者： Bénézech, J. Seelinger, L. Reinarz, A. Dodwell, T.J. Butler, R. Scheichl, R. Material & Structure Research Centre University of Bath Bath United Kingdom Interdisciplinary Center for Scientific Computing Institute for Applied Mathematics Heidelberg University Heidelberg Germany Department of Computer Science Durham University Durham United Kingdom Institute of Data Science and AI University of Exeter Exeter United Kingdom DigiLab Exeter United Kingdom

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Liver-Tumor Detection Using CNN ResUNet

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computers, Materials & Continua 2021年第5期67卷 1899-1914页

作者： Muhammad Sohaib Aslam Muhammad Younas Muhammad Umar Sarwar Muhammad Arif Shah Atif Khan MIrfan Uddin Shafiq Ahmad Muhammad Firdausi Mazen Zaindin Department of Computer Science Government College University FaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science Islamia College PeshawarPeshawarKPPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11421Saudi Arabia

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics *** are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the *** tests are costly and *** paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResUNet model that can be helpful for the early diagnose of tumor instead of conventional *** existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResUNet,an updated version of U-Net and ResNet Models that utilize the service of Residential *** apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected *** results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.

关键词： Liver tumor diagnose ResUNet CNNs segmentation

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Fit and Forecasting Indonesia Banking Stock Price During Covid-19 Pandemic Based on Logistic Differential Equation System 22

Fit and Forecasting Indonesia Banking Stock Price During Cov...

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Proceedings of the 2022 4th International Conference on E-Business and E-Commerce Engineering

作者： Viska Noviantri Andree Sulistio Chandra Yuhani Yusof Mathematics Department School of Computer Science Bina Nusantara University Indonesia Mathematics and Computer Science Department School of Computer Science Bina Nusantara University Indonesia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Malaysia

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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