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3rd International Conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Abdul Ameer, Sabah Auda Khalid, Raed Al Mansor, Ali H. O. Singh, Pardeep Ahl Al Bayt University Kerbala Iraq Medical Technical College Al-Farahidi University Baghdad Iraq Al Zahrawi University College Department of Optical Techniques Karbala Iraq Graphic Era Hill University Deptt. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350300710

Based on S-BERT pre-trained embeddings, this body of work suggests an approach to sentiment analysis using a convolutional neural network (CNN). GloVe and Word2Vec, utilizing the IMDB dataset. The results of our testing showed that the CNN algorithm we built had the highest accuracy at 89.8 percent, outperforming the GloVe and Word2Vec models, which were considered gold standards at the time. During our research on ablation, we noticed that replacing bigrams or trigrams with N-grams can result in improved model performance. In addition, we used a sentiment lexicon to provide context to the text data, which helped improve the model's accuracy. Our study has demonstrated that sentiment analysis can be performed using S-BERT pre-trained embeddings in combination with a CNN model. This strategy has the potential to outperform both standard machine learning approaches and commonly used word embedding models. When these factors are considered, our suggested strategy of using S BERT pre-trained embeddings shows significant potential in real-world applications where sentiment analysis is critical. © 2023 IEEE.

关键词： Sentiment analysis

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Efficient Service Provisioning in Fog computing for Multimodal Fall Detection Systems 4

Efficient Service Provisioning in Fog Computing for Multimod...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Nivarthi, Sarangapani Aher, Vijaya N. Kuppuraj, T. Punia, Ankit Rajiv Gandhi, N. Khanna, Lalit School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641 021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331518981

Our capstone project makes use of an actual-world app that can be efficiently managed by an Edge-Fog-Cloud system. Fall detection is an issue that we have taken up and aim to solve by providing decisions with the lowest feasible delay. We propose a three-stage process that makes use of multimodal data (sensors, video, and pictures). An edge device's convolutional neural network infers the visual data used for the first inference. To improve weight distribution and training speed, the neural network is constructed utilizing transfer learning. The optical flow method removes the background noise and focuses just on the individual by representing moving objects as vector fields. A condensed neural network is able to operate on a resource-constrained edge device. After that, the sensor data is classified by the fog layer using SVM, Random Forest (RF) and Gradient Boosting (XGBoost). The fog layer also uses visual and sensor data to make the ultimate fall judgment. The ensemble decision-making process relies on the inferences drawn from the fog and edge layers to arrive at this conclusion. Model construction and durable data storage are carried out at the cloud layer. For more accurate evaluations, our work also deals with false negative rates. To do this, we use augmentation of images on the misclassified photos;this improves the classification and adds value to the dataset. Last but not least, the suggested models are containerized for simple deployment, maintenance, and scalability. With an accuracy of 99%, the proposed technique surpassed all of its competitors, including SVM, XGBoost. Every approach has its advantages, but the Proposed technique clearly outperforms the others and is hence the best option for this specific job. © 2024 IEEE.

关键词： Decision trees

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Predicting methane adsorption ability of MOFs based on the crystal structural information by convolutional neural network 5

Predicting methane adsorption ability of MOFs based on the c...

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5th International Conference on Material Science and Technology, ICMST 2025

作者： Lu, Gang Jiang, Xiuyi Yang, Qingyuan Zhao, Zhengyang Gu, Weiwei Li, Ruiqi College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9781510691742

High-throughput computational screening (HTCS) methods have been widely used in addressing the challenges of screening metal-organic framework (MOF) materials with high performance from large datasets. Accurately predicting the performance of MOFs with hypothetical structures, such as adsorption capacity for methane, can be a great help to HTCS. Traditional molecular dynamics simulation methods are computationally cumbersome and costly. Feature engineering-based machine learning methods have a high reliance on expert experience to extract effective features, some of which can be time-consuming to calculate. Although some classical machine learning models can already achieve good performance, the heavy burden for features computation prior to model training would eventually limit their practical applications, flexibility and generality. In this paper, an approach is proposed for predicting the methane adsorption ability of MOFs directly from their crystal structural information recorded in Crystallographic Information File (CIF)-formatted files, without the need for molecular dynamics simulation or calculating any physical or chemical properties. By expressing the structural information in the crystal cells of MOFs as matrices, a deep convolutional neural network model based on residual structure is applied to train an end-to-end model to predict their methane adsorption ability. The experimental results show that the model accomplishes the prediction task with high accuracy, giving the mean square correlation coefficient (R2) as 0.925 and the mean absolute error (MAE) as 22.28 cm3g-1, which demonstrates a good performance over baselines. © 2025 SPIE. All rights reserved.

关键词： Convolutional neural networks

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Brahmi Script Recognition Using Optimized Convolutional neural network with Random Forest Classifier

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International Journal of High Speed Electronics and Systems 2025年第0期

作者： Jain, Trang Jain, V.K. Jain, A. College of Computing Sciences and It Teerthanker Mahaveer University U.P. India Koneru Lakshmaiah Educational Foundation Vaddeswaram A.P. Guntur522502 India

optical Character Recognition (OCR) is widely used to digitize printed documents, extract information from forms, automate data entry, and enable text recognition in applications ranging from license plate recognition to handwritten document conversion. Machine learning and deep learning models have recently improved OCR performance, however hyperparameter tuning remains an issue. To solve this, this paper proposes an efficient method for recognizing Brahmi script characters that combines a Convolutional neural network (CNN) with a random forest classifier. First, a CNN-based autoencoder extracts features from Brahmi script images, which are then input into the random forest classification model. The hyperparameters of both models are optimized using a genetic algorithm (GA). Extensive experimental results reveal that the proposed approach achieves a significantly better accuracy of 97%, over competitive models. © 2025 World Scientific Publishing Company.

关键词： Convolutional neural networks

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Unconventional computing With Photonic Oscillator networks

Unconventional Computing With Photonic Oscillator Networks

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作者： Mostafa Honari Latifpour City University of New York

The ever-increasing demand for data processing and the challenges in scaling traditional computing architectures are driving intensive research into alternative computing paradigms. optical computing has garnered renewed attention since the 2010s, driven by its potential to accelerate specialized computational tasks such as combinatorial optimization and neural networks. Coherent light sources including lasers and parametric oscillators have been around for decades and become indispensable tools in modern technology, but these photonic oscillators are also nonlinear dynamical systems that can exhibit emergent, complex phenomena, especially when coupled in arrays. These nonlinear optical systems have recently been shown to be capable of doing computation by emulating spin models. Specifically, an optical realization of the Ising model known as the Coherent Ising Machine has attracted attention for its potential capability of solving the NP-hard Ising problem. This has opened up interesting questions about the opportunities that optical oscillator networks can offer as combinatorial optimization solvers. This thesis primarily explores the potential of networks of nonlinear photonic oscillators for computation. First, we show that a Potts solver can be realized by utilizing multi-stable states of an OPO network, therefore generalizing the Ising machine. Next, through a series of numerical and theoretical investigations, we examine the potential of a coupled laser array as an XY spin model simulator. We show that, under certain conditions, such a system can accurately model the XY Hamiltonian while giving high-quality solutions to the XY optimization problem. Recognizing that we are dealing with classical physical systems, we took inspiration from the dynamics to develop a photonic-inspired algorithm as a heuristic to solve combinatorial optimization problems on digital hardware. We examine the effectiveness of this algorithm and demonstrate its capability in approximat

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Lightening-Transformer: A Dynamically-operated optically-interconnected Photonic Transformer Accelerator 30

Lightening-Transformer: A Dynamically-operated Optically-int...

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30th IEEE International Symposium on High-Performance Computer Architecture (HPCA)

作者： Zhu, Hanqing Gu, Jiaqi Wang, Hanrui Jiang, Zixuan Zhang, Zhekai Tang, Rongxing Feng, Chenghao Han, Song Chen, Ray T. Pan, David Z. Univ Texas Austin Austin TX 78712 USA MIT Cambridge MA 02139 USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9798350393132;9798350393149

The wide adoption and significant computing resource cost of attention-based transformers, e.g., Vision Transformers and large language models, have driven the demand for efficient hardware accelerators. While electronic accelerators have been commonly used, there is a growing interest in exploring photonics as an alternative technology due to its high energy efficiency and ultra-fast processing speed. Photonic accelerators have demonstrated promising results for convolutional neural networks (CNNs) workloads, which predominantly rely on weight-static linear operations. However, they encounter challenges when it comes to efficiently supporting attention-based Transformer architectures, raising questions about the applicability of photonics to advanced machine-learning tasks. The primary hurdle lies in their inefficiency in handling the unique workloads inherent to Transformers, i.e., dynamic and full-range tensor multiplication. In this work, we propose Lightening-Transformer, the first light-empowered, high-performance, and energy-efficient photonic Transformer accelerator. To overcome the fundamental limitation of existing photonic tensor core designs, we introduce a novel dynamically-operated photonic tensor core, DPTC, consisting of a crossbar array of interference-based optical vector dot-product engines, supporting highly parallel, dynamic, and full-range matrix multiplication. Furthermore, we design a dedicated accelerator that integrates our novel photonic computing cores with photonic interconnects for inter-core data broadcast, fully unleashing the power of optics. The comprehensive evaluation demonstrates that Lightening-Transformer achieves >2.6x energy and >12x latency reductions compared to prior photonic accelerators and delivers the lowest energy cost and 2 to 3 orders of magnitude lower energy-delay product compared to the electronic Transformer accelerator, all while maintaining digital-comparable accuracy. Our work highlights the immense potential

关键词： Algorithm-Architecture Co-design Attention Domain-Specific Accelerator optical neural network Photonic Accelerator Transformer

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Towards Robust Homography Estimation for Forward-Motion Panorama for Multi-camera Wireless Capsule Endoscopy Videos 1

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16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Oliveira, Marina Araujo, Helder Fac Sci & Technol Inst Syst & Robot Dept Elect & Comp Engn P-3004531 Coimbra Portugal

ISBN: (数字)9783031670886

ISBN: (纸本)9783031670879;9783031670886

A noteworthy challenge in Wireless Capsule Endoscopy is the significant time and expertise required by medical professionals to accurately interpret its videos and detect anomalies. The combination of a large number of frames to analyze within each video coupled with poor image quality contributes to low lesion detection rates. To address these issues, our study explores a methodology for the construction of local forward and/or backward-motion panoramic overviews from videos rendered from a synthetic tubular model created in Blender. This approach aims to consolidate essential information from multiple frames for the purpose of lesion detection and localization. The mosaicing process is explored by computing global homographies between sequential frames and three methods for homography estimation are assessed. This study compares the resulting local panoramas obtained with our method with those produced using a deep neural network approach, utilizing three different models for image quality assessment.

关键词： Capsule movement Deep learning Feature-extraction panorama optical flow Wireless capsule endoscopy

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Drug-target affinity prediction based on bilinear networks and protein large language model 3

Drug-target affinity prediction based on bilinear networks a...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Tan, Qingyu Li, Zhinong Bu, Dechao Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Henan Zhengzhou450001 China Institute of Computing Technology Chinese Academy Beijing100190 China

ISBN: (纸本)9781510681279

Assessing the binding affinity of pharmaceutical agents to their respective protein targets is a crucial phase in the formulation of new drugs. Current research on prediction algorithms based on deep learning is extensive but primarily focuses on improvements in the representation of proteins and drug molecules, neglecting the representation of interactions between proteins and small molecule drugs, which leads to a lack of biological interpretability. Moreover, the rapid development of protein large language models has introduced new methods for protein representation. Consequently, this study introduces a novel approach for forecasting drug-target affinity, employing bilinear networks coupled with protein large language models, designated as BplmDTA. Initially, BplmDTA employs a protein language model to obtain feature vectors for the target;it also uses extended connectivity fingerprints (ECFP) to represent drug molecules, thereby obtaining drug feature vectors from the constructed embedding layer. Subsequently, the model calculates the interaction between the two feature vectors using a bilinear network. Finally, the affinity between the drug and the target is predicted through a multi-layer feedforward neural network. The findings from the conducted experiments demonstrate that the introduced model surpasses current techniques in forecasting drug-target interactions, offering enhanced efficacy and interpretability. © 2024 SPIE.

关键词： Proteins

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A DDoS Attack Detection Method Based on AE network in The Internet of Vehicles 3

A DDoS Attack Detection Method Based on AE Network in The In...

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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Shen, ShiWen Ning, YuQiao Yu, MingMing Guo, Zhen Xue, ShiHao Wu, QingYang China Automotive Technology & Research Center. Ltd China

ISBN: (纸本)9781510663473

With the rapid development of 5G technology, the intelligent and connected vehicle (ICV) technology is also evolving and expanding its application scenarios. In order to achieve lower latency and reduce the network load caused by massive data reflow in ICV, MEC (Mobile Edge computing) technology is introduced to support ICV communication. While MEC technology brings a good experience to users, more and more attacks against Telematics come along, the most common of which is DDoS attacks, which can bring huge losses to Telematics systems. Based on this, this paper proposes a DDoS attack detection method based on SAE neural network. The method uses the stacked Auto-encoder-based model proposed in the paper to detect network traffic in the Telematics network, feeds the traffic data into the test model, and determines whether the automotive network system is under DDOS attack based on a threshold value. The DDoS attack is detected using the method proposed in the paper, with high detection rates in the training and test sets and stable models. Better experimental results were also obtained by later changing the number of hidden layers in the SAE network to detect DDoS attacks. Comparing the method in this paper with the SVM and CNN methods, the experimental results show that the DDoS attack detection method based on SAE networks works best. © 2023 SPIE.

关键词： Mobile edge computing

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Graph-based saliency and ensembles of convolutional neural networks for glaucoma detection

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IET IMAGE PROCESSING 2021年第3期15卷 797-804页

作者： Serte, Sertan Serener, Ali Near East Univ Dept Elect & Elect Engn North Cyprus Via Mersin 10 Nicosia Turkey

Glaucoma, after cataracts, is the second leading cause of worldwide vision loss. An ophthalmologist may use various tools and methods to diagnose a glaucomatous eye. Computer-aided methods involving deep convolutional neural networks also made it recently possible to detect glaucoma on fundus images. Previous studies traditionally trained a single convolutional neural network for automatic detection of glaucoma. In this study, a more advanced way of accurate automated glaucoma recognition is proposed. First, a graph-based saliency region detection is used to crop the optic disc and remove the redundant parts of the fundus images. Then, four methods are used to ensemble convolutional neural network models comprising up to three deep learning architectures for glaucoma classification. The detection performance of this model is better than a recent study that used the same dataset. It is also as good as, or better than, the results reported by other recent research in the literature on glaucoma detection.

关键词： Anatomy and optics of the eye Mechanical properties of tissues and organs Image recognition optical and laser radiation (biomedical imaging/measurement) Computer vision and image processing techniques optical and laser radiation (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing neural nets

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