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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Enhancing Highway Safety with LSTM-based Vehicle Intention Prediction (HVIP)

Enhancing Highway Safety with LSTM-based Vehicle Intention P...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Amer, Lujina Elias, Catherine M. C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science and Engineering Department Egypt

ISBN: (纸本)9798350367560

Predicting vehicles' motion on highways has become crucial for enhancing road safety and traffic flow. Deep learning, which reached exceptional results in various applications, is now the leading approach for vehicle motion prediction. This work presents a deep learning-based model using Long Short-Term Memory (LSTM) networks to classify vehicle intentions into lane-keeping, left lane-changing, and right lane-changing. Utilizing the PREVENTION dataset, which provides naturalistic driving data, a sequence of centre points, longitudinal distances, lateral distances and yaw angles of a vehicle has been extracted to train the model to predict lane changes effectively. The model consists of three LSTM layers, a dense output layer and a drop out added between layers to prevent overfitting. Experiments focusing on optimizing the model parameters, learning rate, sequence length, and batch size, were conducted to determine their impact on prediction effectiveness. The best-performing model showed significant results in lane changing prediction, achieving an accuracy of 84%, precision of 89%, and recall of 82%. Future work will aim to train the model on more complex features, to account for vehicle inter-dependencies and interactions, and test model on various highway scenarios for a more reliable system. © 2024 IEEE.

关键词： Vehicle safety

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The EGY-DRiVeS Dataset: Illuminating the Dynamics of Egyptian Road Traffic Behaviors

The EGY-DRiVeS Dataset: Illuminating the Dynamics of Egyptia...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Saadawy, Zeyad H. Elfar, Mohamed A. Aly, Menna M. Adel, Momen Bahy, Mohamed A. Elias, Catherine M. C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science and Engineering Department Egypt

ISBN: (纸本)9798350367560

This paper presents a comprehensive dataset of Egyptian roads integrated with chaotic scenarios captured by EGY-DRiVeS' lab golf car to aid in developing the autonomous driving experience. Using Velodyne 3D laser scanners, C922 Pro HD webcams, and iPhone 11 Pro cameras, a cumulative recording time of 3 hours and 48 minutes was documented. This included 1 hour and 15 minutes of LiDAR recordings, 1 hour and 45 minutes of camera recordings, and 48 minutes and 13 seconds of synced recordings. The dataset encompasses diverse scenarios and objects, ranging from urban environments with dynamic and static objects to rural highways. In-campus roads and intersections are also included, providing real-world scenarios such as vehicular and pedestrian behavior within campus premises. Each part of the dataset is annotated with 2D bounding boxes offering detailed information about vehicles, pedestrians and road objects. The provided data are calibrated, synchronized, and timestamped. Alongside the dataset, data format specifications, annotated data samples, and all supplementary recordings are provided upon request. © 2024 IEEE.

关键词： Network security

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TriMoFaceNet: A Trimmed MobileFaceNet Architecture for Resource-constrained Environments 6

TriMoFaceNet: A Trimmed MobileFaceNet Architecture for Resou...

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6th International Conference on Image, Video Processing, and Artificial Intelligence, IVPAI 2024

作者： Prakash, Pritesh Singh, Prinsh Kumar Jerripothula, Koteswar Rao Central Research Lab Bharat Electronics Ltd. Ghaziabad India Department of Electrical Engineering IIT Kanpur India Department of Computer Science and Engineering IIIT-Delhi India

ISBN: (纸本)9781510681781

As one of the most popular vision applications, face verification systems can be needed in all kinds of scenarios, including resource-constrained environments. In this paper, we propose a trimmed variant of MobileFaceNet, which can perform as well as MobileFaceNet but with fewer memory and computational requirements. We essentially make two changes to the bottlenecks: reversing the expansion-projection process and adding one more depth-wise convolution layer. While the first one significantly reduces the number of parameters and calculations, the second one increases them. We try to strike a balance between the two to have a complex enough network architecture that can perform face verification as well as the original one. We could cut down the model size by almost a quarter in this manner, and we name the resultant network TriMoFaceNet. © 2024 SPIE.

关键词： Network architecture

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Towards a domain-driven distributed reference architecture for big data systems 29

Towards a domain-driven distributed reference architecture f...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Ataei, Pouya Litchfield, Alan School of Engineering Computer and Mathematical Sciences New Zealand Service and Cloud Computing Research Lab New Zealand

ISBN: (纸本)9781713893592

The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user’s capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges. © 2023 29th Annual Americas Conference on Information Systems, AMCIS 2023. All rights reserved.

关键词： Big data big data architecture big data reference architecture data architecture data engineering

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Facial Image Manipulation Detection Using Cellular Automata and Transfer Learning 1

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The 9th International Conference on Mathematics and Computing (ICMC 2023)

作者： Chakraborty, Shramona Chowdhury, Dipanwita Roy Crypto Research Lab Department of Computer Science and Engineering Indian Institute of Technology Kharagpur721 302 India

ISBN: (数字)9789819930807

ISBN: (纸本)9789819930791

In digital media forensics, detecting manipulated facial images is a crucial topic. With the advancement of synthetic face generation technologies, different types of face morphing have raised serious concerns in social media implications. Hence, we have worked on detecting the manipulated face image thoroughly. Despite just using a multi-task learning approach, we propose a facial image manipulation detection scheme using cellular automata (CA) and transfer learning (TL) as feature extractors to enhance the classifier model’s feature maps. The extracted features point out the informative regions to refine the binary classification power. We have collected datasets that contain diverse facial forgeries. We have also built our dataset, keeping every possible morphing way in mind. We have analyzed the datasets thoroughly and demonstrated that using a combination of CA and TL improves manipulated fake detection on different morphing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Feature extraction

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Neural Implicit representations for 3D scene modeling and visualization using multipass SAR 15

Neural Implicit representations for 3D scene modeling and vi...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Sugavanam, Nithin Ertin, Emre Jamora, Jan Rainer Department of Electrical and computer engineering The Ohio State university ColumbusOH43201 United States Air-force Research Lab United States

ISBN: (纸本)9783800762873

Synthetic aperture radar (SAR) collects samples of the 3D Spatial Fourier transform on a two-dimensional surfaces corresponding to the backscatter data of wideband pulses launched from different look angles along the synthetic aperture. In this paper we present a coordinate-based multi-layer perceptron (MLP) that enforces the smooth surface-prior and models the complex-valued scattering coefficients on the surface of the imaged object. The resulting implicit neural representation is capable of predicting phase history data for arbitrary apertures in the span of the training data. The 3D surface is represented using a signed distance function, while the scattering coefficients are represented using real and imaginary channels. Since estimating a smooth surface from a sparse and noisy point cloud is an ill-posed problem, in this work, we build on our previous work to regularize the surface estimation by sampling points from the implicit surface representation during the training step. The loss function also incorporates the consistency between the predicted complex-valued scattering coefficients and the true scattering coefficients. We validate the model's ability to represent target scattering using simulated data of Civilian vehicle data domes. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language 30

Exploring the Potential of Large Language Models (LLMs) for ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Subedi, Bipesh Regmi, Sunil Bal, Bal Krishna Acharya, Praveen Information and Language Processing Research Lab Department of Computer Science & Engineering Kathmandu University Nepal School of Computing Dublin City University Ireland

ISBN: (纸本)9782493814104

Large Language Models (LLMs) have made significant advancements in Natural Language Processing (NLP) by excelling in various NLP tasks. This study specifically focuses on evaluating the performance of LLMs for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging for a low-resource language, Nepali. The aim is to study the effectiveness of these models for languages with limited resources by conducting experiments involving various parameters and fine-tuning and evaluating two datasets namely, ILPRL and EBIQUITY. In this work, we have experimented with eight LLMs for Nepali NER and POS tagging. While some prior works utilized larger datasets than ours, our contribution lies in presenting a comprehensive analysis of multiple LLMs in a unified setting. The findings indicate that NepBERTa, trained solely in the Nepali language, demonstrated the highest performance with F1-scores of 0.76 and 0.90 in ILPRL dataset. Similarly, it achieved 0.79 and 0.97 in EBIQUITY dataset for NER and POS respectively. This study not only highlights the potential of LLMs in performing classification tasks for low-resource languages but also compares their performance with that of alternative approaches deployed for the tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Translate-and-Revise: Boosting Large Language Models for Constrained Translation 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Huang, Pengcheng Mu, Yongyu Wu, Yuzhang Li, Bei Xiao, Chunyang Xiao, Tong Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China JP Morgan United Kingdom

ISBN: (纸本)9780000000002

Imposing constraints on machine translation systems presents a challenging issue because these systems are not trained to make use of constraints in generating adequate, fluent translations. In this paper, we leverage the capabilities of large language models (LLMs) for constrained translation, given that LLMs can easily adapt to this task by taking translation instructions and constraints as prompts. However, LLMs cannot always guarantee the adequacy of translation, and, in some cases, ignore the given constraints. This is in part because LLMs might be overly confident in their predictions, overriding the influence of the constraints. To overcome this overiding behaviour, we propose to add a revision process that encourages LLMs to correct the outputs by prompting them about the constraints that have not yet been met. We evaluate our approach on four constrained translation tasks, encompassing both lexical and structural constraints in multiple constraint domains. Experiments show 15% improvement in constraint-based translation accuracy over standard LLMs and the approach also significantly outperforms neural machine translation (NMT) state-of-the-art methods. © 2024 China National Conference on Computational Linguistics.

关键词： Neural machine translation

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