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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cui, Shuai Zhang, Xuan Nie, Zizheng Jiang, Haitao Cui, Guishan Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Yanbian University Department of Geography and Ocean Sciences Yanji China

ISBN: (纸本)9798350386226

The protein-ligand affinity prediction task aims to predict the binding strength of small molecule ligands to specific proteins, which is crucial in the fields of drug design and molecular biology, and can accelerate the drug discovery. The structure complementarity between protein and ligand plays a critical role in determining binding strength, but most of current deep learning-based affinity prediction models usually extracted the features of protein and ligand by these two detached modules. which limits the exchange of information for capturing interactions and struggles to capture proteins' important residues. To address these limitations. we introduce CIP. which takes the combination of GNN, Conditional Updating and Proximity Embedding for the first time. Compared to existing models, CIP has several significant advantages. First, Conditional updating modifies the ligand's local features based on the protein's global features, and vice versa, enhancing structural complementarity to capture intricate interactions. Second, encoding the relative distances between proximal residue-atom pairs highlights critical residues. Additionally, our model integrates covalent and noncovalent interactions to obtain more comprehensive graph representations. Experiments on the PDB-bind 2016 benchmark demonstrate that CIP outperforms the original method with improvements of 2.3%, 3.2%, 2.8%, 3.8%, and 3.2% across five baselines. Furthermore, visualization results reveal that CIP effectively captures intricate interactions and crucial *** implemented code and dataset are available online at https://***/xfcui/CIP. © 2024 IEEE.

关键词： Drug discovery Graph neural network (GNN) Protein-Ligand Affinity Prediction

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Simultaneous Speech Extraction for Multiple Target Speakers Under Meeting Scenarios

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Zeng, Bang Suo, Hongbin Wan, Yulong Li, Ming School of Computer Science Wuhan University Wuhan430027 China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan Jiangsu215316 China Data & amp AI Engineering System OPPO Beijing100125 China

The common target speech separation directly estimates the target source, ignoring the interrelationship between different speakers at each frame. We propose a multiple-target speech separation (MTSS) model to simultaneously extract each speaker’s voice from the mixed speech rather than just optimally estimating the target source. Moreover, we propose a speaker diarization (SD) aware MTSS system (SD-MTSS). By exploiting the target speaker voice activity detection (TSVAD) and the estimated mask, our SD-MTSS model can extract the speech signal of each speaker concurrently in a conversational recording without additional enrollment audio in advance. Experimental results show that our MTSS model achieves improvements of 1.38 dB signal-to-distortion ratio (SDR), 1.34 dB scale-invariant signal-to-distortion ratio (SISDR), and 0.13 perceptual evaluation of speech quality (PESQ) over the baseline on the WSJ0-2mix-extr dataset, separately. The SD-MTSS system makes a 19.2% relative speaker dependent character error rate reduction on the Alimeeting dataset. © Shanghai Jiao Tong University 2024.

关键词： Speech analysis

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Revolutionizing Brain Tumour Prediction: A Pioneering GAN-based Framework for Synthetic data Generation 7

Revolutionizing Brain Tumour Prediction: A Pioneering GAN-ba...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Kumpatla, Gautam Veresi, Hemanth Bala Subrahmanyam, P. Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350341485

Medical analysis is a key component of contemporary healthcare since it helps doctors diagnose patients accurately to plan and track their treatments. Accurate identification of brain tumors is essential for doctors to treat patients swiftly and efficiently. This research investigates a novel approach to overcome the constraints imposed by scarce and sensitive medical data, generating synthetic images of brain tumors using Generative Adversarial Networks (GANs). The proposed method has the potential to enhance activities involving medical processing, which will aid in making diagnoses and formulating treatment plans. The preliminary findings demonstrate its potential relevance to a wider variety of medical processing tasks, demonstrating that this augmentation method yields significant benefits. © 2023 IEEE.

关键词： Generative adversarial networks

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Prediction Of Crop: Based On Various Predictive Algorithmic Models 5

Prediction Of Crop: Based On Various Predictive Algorithmic ...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Vaishnnave, M.P. Kamaitachi, S. Bangaru Kumar, C. Ramesh Premkumar, S. Department Of Computer Science And Engineering TamilNadu Chennai India Galgotias University School Of Computing Science And Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350330861

As per the Niti Aayog report, the Government of India is planning to double the income of farmers by 2022. In another report Niti Aayog depicted that, there is a 3.6% decrease in total engagement in agriculture from 2001 to 2022. One of the reasons will be so many people are leaving farming is that soil fertility rate, water level and climate are changing in India and its subcontinent. Due to this, crops grown by the farmers don't get a surplus outcome after harvesting. The model will use Naïve Bayes algorithm, which comes under supervised learning algorithm for the prediction for most suitable crop. The seed data of the crops will be collected along with relevant parameters such as humidity, moisture, and temperature, which will aid the crops for effective and efficient growth. In the web application, the users will enter the parameter like temperature, location etc. and then the application will predict the most likable crop for better farming. © 2023 IEEE.

关键词： Crops

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Performance Analysis of Multiple HAPS-Based Hybrid FSO/RF Space-Air-Ground Network 16

Performance Analysis of Multiple HAPS-Based Hybrid FSO/RF Sp...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Singh, Deepshikha Swaminathan, R. Marrapu, Aravind Madhukumar, A.S. Indian Institute of Technology Indore Department of Electrical Engineering India School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350383119

Sixth generation (6G) networks are anticipated to support increasingly data-hungry applications by expanding the network capabilities and connectivity. Free space optics (FSO) communication with space-air-ground (SAG) network becomes a potential solution to meet the enormous traffic demands and ubiquitous connectivity requirement in 6G networks. In spite of various advantages offered by FSO communication such as high bandwidth availability, large unlicensed spectrum, better security, etc., the FSO link exhibits a high degree of responsiveness to atmospheric turbulence, attenuation, and pointing errors. Thus to mitigate the severe atmospheric effects, we propose a multiple high-altitude platform station (HAPS)-based SAG network, where HAPS functions as a decode-and-forward (DF) relay node between ground station and satellite. We take into account a hybrid FSO/radio frequency (RF) link for connecting ground station and HAPS nodes and an FSO link between HAPS and satellite. The performance analysis of the proposed SAG network involves evaluating the outage probability and average symbol error rate (SER) considering the presence of pointing errors and assuming Málaga distribution in case of FSO and shadowed k - μ fading distribution in case of RF link. Furthermore, asymptotic analysis is conducted to determine the diversity gain of the proposed system. Numerical results demonstrate that the proposed multiple HAPS-based hybrid FSO/RF SAG network outperforms all other existing HAPS-based SAG network models in the literature. © 2024 IEEE.

关键词： DF relaying free space optics (FSO) high-altitude platform station (HAPS) hybrid FSO/RF performance analysis

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Face anti-spoofing based on multi-modal and multi-scale features fusion

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The Journal of China Universities of Posts and Telecommunications 2022年第6期29卷 73-82页

作者： Kong Chao Ou Weihua Gong Xiaofeng Li Weian Han Jie Yao Yi Xiong Jiahao School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550025China Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province Guizhou Institute of TechnologyGuiyang 550003China Guizhou Science and Technology Information Center Department of Science and Technology of Guizhou ProvinceGuiyang 550002China

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.

关键词： face anti-spoofing multi-modal fusion multi-scale fusion self-attention network(SAN) feature pyramid network(FPN)

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Meta-analysis of quantum convolutional neural networks for automated tuberculosis screening on chest x-rays

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Multimedia Tools and Applications 2025年 1-27页

作者： Rana, Anurag Bhaglani, Dimple Kumar Vaidya, Pankaj Hu, Yu-Chen Computers and Data Sciences Yogananda School of AI Shoolini University Himachal Pradesh Solan India Department of Pulmonary Medicine Indira Gandhi Medical College Himachal Pradesh Shimla India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

The problem of detecting tuberculosis (TB) from chest X-ray (CXR) images was addressed by developing a convolutional neural network (CNN) model. The CNN was trained using two publicly available chest radiograph datasets from Himachal Pradesh, India. To evaluate its performance, the CNN model was compared with a transfer learning-based technique that utilized several pre-trained CNNs. The results showed that the CNN model outperformed the transfer learning approach. The study further explored the integration of quantum algorithms in image classification. It introduced a novel hybrid quantum–classical computing paradigm, wherein classically challenging elements of an algorithm are handled by a quantum computer. Specifically, a hybrid quantum–classical image classification technique, inspired by CNNs and called the quanvolutional neural network (QNN), was used for diagnosing TB from chest X-rays. This new QNN variant employed novel enhanced quantum representation (NEQR) image encoding to transform pixel values into quantum states. The QNN model was trained on the same two chest radiograph datasets as the CNN model. The QNN demonstrated superior performance to the CNN model, with a validation accuracy of 87% during training. These findings suggest that hybrid quantum–classical image classification algorithms can surpass traditional methods in diagnosing tuberculosis from chest X-rays. From the results, the Exception, ResNet50, and VGG16 models performed exceptionally well in Quantum CNN models with image augmentation, achieving over 89% accuracy, precision, sensitivity, and F1-score for TB detection. The study suggests that a larger dataset could improve model precision and reliability. Differences in outcomes compared to previous studies may stem from using simulators of flawless quantum computers in earlier research. This study highlights the potential of integrating a quantum circuit with classical CNNs for TB diagnosis from chest X-rays using NEQR image encoding, pr

关键词： Quantum computers

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Comparing Local Binary Pattern and Gray Level Co-occurrence Matrix for Feature Extraction in Diabetic Retinopathy Classification

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Procedia computer science 2023年 227卷 355-363页

作者： Nathanael Matthew Makmur Felicia Kwan Astrid Dewi Rana Felix Indra Kurniadi Computer Science Department Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia

Diabetic retinopathy, a condition characterized by retinal damage and vision loss, is a prevalent complication of diabetes arising from elevated blood sugar levels. With a growing number of individuals affected, efficient and accurate diagnosis is crucial. This study aims to implement and compare the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) feature extraction techniques, which have demonstrated success in prior research. The comparison will provide a comprehensive under- standing of the image features, extract relevant data, and improve the performance of the image analysis pipeline for diabetic retinopathy classification. The result showed that from three scenarios the best accuracy provided by Support Vector Machine with the accuracy score between 73% until 74%, however, other algorithm have little difference which the result on 73%.

关键词： Diabetic Retinophaty GLCM LBP computer Vision

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An Optimized Hybrid Deep Learning Framework for Monitoring Botnet Attacks in IoT Networks 5

An Optimized Hybrid Deep Learning Framework for Monitoring B...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Manimaran, A. Sathish Kumar, P.J. Kartheesan, L. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Technical Sciences Department of Electronics and Communication Engg Chengalpattu India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Department of Computer Science and Engineering Institute of Science and Technology Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engg Sriperumudur India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The serious threat of botnet attacks in the IOT world today can be effectively addressed with deep learning (DL). However, to train the model, large and complex data sets are required, which adds cost and necessitates expensive hardware to perform intricate mathematical operations. The IoT plays a significant challenge in memory constrained by implementing the DL. The hybrid Principal Component Analysis (PCA) and Autoencoder is proposed to measures the performance to lower the IoT network traffic data and consumes bulky information. Following that, reduced datasets are examined and trained using hybrid Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) algorithms. The hybrid deep learning algorithms attain the feature to enhance the precision, accuracy, recall, and F1 scores 96.51%, 100%, 96.51%, and 98.22%, which were higher than the conventional methods. The model's performance is analyzed using extensive experiments. © 2024 IEEE.

关键词： Bot (Internet)

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