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A roadmap to data science: Background, future, and trends

International Journal of Intelligent Information and Database Systems 2021年第3期14卷 277-293页

作者： Khder, Moaiad Ahmad Fujo, Samah Wael Sayfi, Mohammad Adnan Department of Computer Science Applied Science University Bahrain Department of Computing Engineering and Technology Faculty of Applied Sciences University of Sunderland United Kingdom

Data science is a combination of several disciplines that aims to get accurate insights from a bunch of data, develop the technology, and algorithm to solve the complicated problems analytically. Today, data science plays a massive role in our life, and researchers realise how it is essential. Numerous research studies on data science have been published in recent years, but each focus on specific issues, such as data science and its impact on business, manufacturing, academia, and healthcare. This paper will present a roadmap of data science to benefit the readers to know about this field and realise how it is essential in several areas. In addition, it clarifies the mechanism of how data science work and what are the capabilities of the data scientist to be able to work in this field. It also shows the trends and future work of data science. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Big data

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

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

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Visual Analytics and Data Visualization (ICVADV), International Conference on

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Deep learning Ciphers Accuracy Computational modeling Visual analytics Neural networks Transformers Encryption Pattern recognition Convolutional neural networks

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Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System 27

Optimizing Forest Fire Prevention: Intelligent Scheduling Al...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Ecole Supérieure des Sciences Economiques et Commerciales de Tunis Université de Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： algorithms drone fire Forest monitoring

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Attention-Guided Deep Learning Framework For Movement Quality Assessment

Attention-Guided Deep Learning Framework For Movement Qualit...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Aditya Kanade Mansi Sharma Manivannan Muniyandi Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Applied Mechanics Indian Institute of Technology Madras India

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores.

关键词： Training Deep learning Analytical models Predictive models Signal processing Transformers Feature extraction

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Strong Current in Carbon Nanoconductors: Mechanical and Magnetic Stability

SSRN

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SSRN 2024年

作者： Brandbyge, Mads Leitherer, Susanne Papior, Nick Department of Chemistry University of Copenhagen CopenhagenDK-2100 Denmark DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens LyngbyDK-2800 Denmark Department of Physics Technical University of Denmark Kongens LyngbyDK-2800 Denmark

Carbon nanoconductors are known to have extraordinary mechanical strength and interestingmagnetic properties. Moreover, nanoconductors based on one- or two-dimensional carbon allotropesdisplay a very high current-carrying capacity and ballistic transport. Here, we employa recent, simple approach based on density functional theory to analyze the impact of strongcurrent on the mechanical and magnetic properties of carbon nanoconductors. We find that theinfluence of the current itself on the bond-strength of carbon in general is remarkably low comparedto e.g. typical metals. This is demonstrated for carbon chains, carbon nanotubes, grapheneand polyacetylene. We can trace this to the strong binding and electronic bandstructure. On theother hand, we find that the current significanly change the magnetic properties. In particular, wefind that currents in graphene zig-zag edge states quench the magnetism. © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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Strong current in carbon nanoconductors: Mechanical and magnetic stability

arXiv

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arXiv 2024年

作者： Leitherer, Susanne Papior, Nick R. Brandbyge, Mads Department of Chemistry University of Copenhagen CopenhagenDK-2100 Denmark DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens LyngbyDK-2800 Denmark Department of Physics Technical University of Denmark Kongens LyngbyDK-2800 Denmark

Carbon nanoconductors are known to have extraordinary mechanical strength and interesting magnetic properties. Moreover, nanoconductors based on one- or two-dimensional carbon allotropes display a very high current-carrying capacity and ballistic transport. Here, we employ a recent, simple approach based on density functional theory to analyze the impact of strong current on the mechanical and magnetic properties of carbon nanoconductors. We find that the influence of the current itself on the bond-strength of carbon in general is remarkably low compared to e.g. typical metals. This is demonstrated for carbon chains, carbon nanotubes, graphene and polyacetylene. We can trace this to the strong binding and electronic bandstructure. On the other hand, we find that the current significanly change the magnetic properties. In particular, we find that currents in graphene zig-zag edge states quench the magnetism. © 2024, CC BY.

关键词： Magnetic properties

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Multimodal segmentation neural network to determine the cause of damage to grasslands 29

Multimodal segmentation neural network to determine the caus...

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29th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2021

作者： Johenneken, Maximilian Drak, Ahmad Herpers, Rainer Asteroth, Alexander Department of Computer Science Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Institute of Visual Computing Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Faculty of Computer Science University of New Brunswick Fredericton Canada Lassonde School of Engineering York University Toronto Canada Computer Science Department University College London London United Kingdom

ISBN: (纸本)9789532901092

Agricultural fields suffer from damage due to changing climate conditions and wildlife foraging. Damage incurred by wild boar is classified as the main contributing factor to grasslands damage. Such damage results in losses for farmers due to reduced yield potential and repair costs. The extent of wild boar damage is typically estimated manually by using ground based approaches (e.g. GPS measurements), which is a time consuming task with questionable accuracy. Building upon our previous work, we present an autonomous approach to detect and classify the cause of damage to grasslands (wild boar, mole etc.). The approach entails utilizing convolutional neural networks for semantic segmentation of grasslands. An RGB baseline was established, in addition to evaluating multimodal architectures that incorporate different surface model feature representations, leading to a joint representation of spectral and elevation information. Testing and experimentation was performed in real-world grasslands around Bonn, Germany. The results show that incorporating elevation features with late fusion enhances the overall performance of the network over the RGB baselines. © SoftCOM 2021. All rights reserved.

关键词： Semantic Segmentation

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3D Semantic Deep Learning Networks for Leukemia Detection

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computers, Materials & Continua 2021年第10期69卷 785-799页

作者： Javaria Amin Muhammad Sharif Muhammad Almas Anjum Ayesha Siddiqa Seifedine Kadry Yunyoung Nam Mudassar Raza University of Wah Wah CanttPakistan COMSATS University Islamabad Wah CampusPakistan National University of Technology(NUTECH) IJP Road IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and *** cell growth destroys the body’s immune system,and computerized methods play a vital role in detecting abnormalities at the initial *** this research,a deep learning technique is proposed for the detection of *** proposed methodology consists of three *** I uses an open neural network exchange(ONNX)and YOLOv2 to localize *** localized images are passed to Phase II,in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception *** segmented images are used in Phase III,in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify *** proposed methodology is validated on three publically available benchmark datasets,namely ALL-IDB1,ALL-IDB2,and LISC,in terms of different metrics,such as precision,accuracy,sensitivity,and dice *** results of the proposed method are comparable to those of recent existing methodologies,thus proving its effectiveness.

关键词： YOLOv2 darknet53 Bhattacharyya separately criteria ONNX

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SOCIOTECHNICAL EFFECTS OF MACHINE TRANSLATION

arXiv

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arXiv 2025年

作者： Moorkens, Joss Way, Andy Lankford, Séamus ADAPT Centre School of Applied Language and Intercultural Studies Dublin City University Dublin Ireland ADAPT Centre School of Computing Dublin City University Dublin Ireland ADAPT Centre Department of Computer Science Munster Technological University Cork Ireland

While the previous chapters have shown how machine translation (MT) can be useful, in this chapter [Moorkens et al., 2024d] we discuss some of the side-effects and risks that are associated, and how they might be mitigated. With the move to neural MT and approaches using Large Language Models (LLMs), there is an associated impact on climate change, as the models built by multinational corporations are massive. They are hugely expensive to train, consume large amounts of electricity, and output huge volumes of kgCO2 to boot. However, smaller models which still perform to a high level of quality can be built with much lower carbon footprints, and tuning pre-trained models saves on the requirement to train from scratch. We also discuss the possible detrimental effects of MT on translators and other users. The topics of copyright and ownership of data are discussed, as well as ethical considerations on data and MT use. Finally, we show how if done properly, using MT in crisis scenarios can save lives, and we provide a method of how this might be done. © 2025, CC BY.

关键词： computer aided language translation

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Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

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computers in Biology and Medicine 2024年 182卷 109183-109183页

作者： Ullah, Muhammad Sami Khan, Muhammad Attique Albarakati, Hussain Mubarak Damaševičius, Robertas Alsenan, Shrooq Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah24382 Saudi Arabia Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy. © 2024 Elsevier Ltd

关键词： Data fusion

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