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DeepSipred: A deep-learning-based approach on siRNA inhibition prediction 4

DeepSipred: A deep-learning-based approach on siRNA inhibiti...

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4th International conference on Bioinformatics and intelligent Computing (BIC)

作者： Liu, Bin Huang, Huiya Liao, Weixi Pan, Xiaoyong Jin, Cheng Yuan, Ye Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Syngentech Inc Zhongguancun Life Sci Pk Beijing 102206 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Med Robot Res Inst Shanghai Peoples R China

ISBN: (纸本)9798400716645

Using exogenous small interfering RNAs (siRNAs) for gene silencing has become a widespread molecular tool for gene function study and new drug identification. Although the pathway of RNAi to mediate gene expression has been widely investigated, the selection of hyper-functional siRNA with high inhibition remains challenging. In this study, we build a deep-learning-based approach on siRNA inhibition prediction, named DeepSipred. It combines features from sequence context, thermodynamic property, and other expert knowledge together to predict the inhibition more accurately than existing methods. the sequence features from siRNA and local target mRNA are generated via one-hot encoding and pretrained RNA-FM encoding. the convolutions with multiple kernels can detect various decisive motifs in sequence embedding, which may determine the actual inhibition. the thermodynamic features are calculated from Gibbs Free Energy. In addition, the expert knowledge includes those design criteria from previous studies. Benchmarked on large available public datasets, the 10-fold cross-validation results indicate that our predictor achieves the state-of-the-art performance.

关键词： siRNA design inhibition prediction deep neural network

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Research on the Construction of Smart Grid Digital System Based on Big Data Cloud Computing Model optimization Algorithm 4

Research on the Construction of Smart Grid Digital System Ba...

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4th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Li, Zhipeng Liu, Shaobo Zhang, Jianfeng Wang, Jun Shen, Shuo State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Company Digital Division Lanzhou China

ISBN: (纸本)9798350359589

An intelligent power business development method based on digital service information is proposed. At the same time, a complete data mining framework is constructed. Parameters such as distance of adjacent pixels, similarity degree of carrier components and average amplitude were determined by coding and parsing. then the digital business information resources are extracted. the mining method of data cloud in power system based on time series is established. the correlation degree of each information characteristic of middle station is analyzed based on classification and characteristic identification of power system middle station. Experiments show that the algorithm can effectively improve the distribution of data in the middle station in every minute. the standard deviation of the algorithm is reduced and the mining effect is improved. © 2024 IEEE.

关键词： artificial intelligence digital system optimization algorithm smart grid

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Image recognition of soybean leaf disease based on bilateral branching network BBN 4

Image recognition of soybean leaf disease based on bilateral...

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4th International conference on Bioinformatics and intelligent Computing (BIC)

作者： Sun, Jianming Hao, Xuyao Zhao, Mengxin Harbin Univ Commerce Sch Comp Informat & Engn Harbin Peoples R China

ISBN: (纸本)9798400716645

In recent years, with the rapid growth of China's economy, environmental pollution has become more and more serious. Fog and haze weather, water pollution and other factors have led to the occurrence of soybean diseases, seriously affecting the quality and yield of soybean. With the development of computer vision technology and deep learning and gradually applied to the development of agriculture, it is possible to use computers to intelligently diagnose crop diseases. Due to the different incidence of different types of soybean diseases in different regions and at different times, the long tail distribution problem of soybean leaf disease datasets is caused, which leads to the overall performance of disease recognition models. To solve this problem, BBN_IC_MobileNetV3 network model is proposed. the new network is based on MobileNetV3. Firstly, the multi-scale feature extraction module is used to replace the first 3 x 3 convolution layer of the original network to improve the feature extraction ability of the network for different areas of disease spots. Secondly, the CA attention mechanism is used to replace the SE attention mechanism of the original network to distinguish between the target and background. Finally, the improved MobileNetV3 network is combined with the BBN network to improve the recognition accuracy of the tail data by modeling the tail data, so as to improve the overall performance of the model. Experiments show that the overall disease recognition rate of the BBN_IC_MobileNetV3 network model reaches 95.882%, and the recognition accuracy of the tail data is close to 90%, which has been improved compared with the original network.

关键词： Deep learning Long-tail distribution Disease identification Multiscale feature extraction Attention mechanism

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intelligent In-Car Emotion Regulation Interaction System Based on Speech Emotion Recognition 4

Intelligent In-Car Emotion Regulation Interaction System Bas...

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4th International conference on Computer, Control and Robotics (ICCCR)

作者： Yang, Yuhan Zhang, Yan Zhong, Zhinan Dai, Wan Chen, Yunfei Chen, Mo Southeast Univ Sch Mech Engn Nanjing Peoples R China Nanjing Tech Univ Coll Art & Design Nanjing Peoples R China

ISBN: (纸本)9798350373141;9798350373158

With driving emerging as a common mode of transportation, the automotive industry has increasingly prioritized driving safety and experience. A substantial body of research focused on driving safety and the overall travel experience has underscored the pivotal role of emotions. In this article, we introduce an innovative in-car emotion recognition and interaction system, carefully crafted to intelligently respond to the emotional states of drivers. this system captures real-time emotional data through its user input layer and seamlessly integrates it into the technological architecture layer, residing within the vehicle's CPU. Leveraging cutting-edge deep learning models for emotion recognition, the system's outcomes trigger tailored emotion regulation strategies within the interaction feedback layer. Notably, our study introduces a groundbreaking speech fusion feature, MFCCs+, meticulously crafted for driving contexts. Furthermore, we have optimized the driving speech emotion recognition model using 1D-CNN, resulting in a remarkable 10% improvement in recognition accuracy. Subsequent validation experiments affirm the system's effectiveness in enhancing driving safety. In conclusion, the integration of emotion-based interaction solutions holds immense potential for elevating both driving safety and the overall travel experience within intelligent driving scenarios. this innovation promises to shape the future landscape of automotive travel, offering a safer and more enjoyable journey for all.

关键词： Human machine interaction (HMI) automotive travel feature selection speech emotion recognition machine learning emotion regulation

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A Hybrid Deep learning Approach for Brain Tumor Classification 4th

A Hybrid Deep Learning Approach for Brain Tumor Classificati...

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4th International conference on Novel and intelligent Digital Systems

作者： Cruz, Matthew John F. Sino Caro, Jaime D. L. Univ Philippines Diliman Quezon City Philippines Pangasinan State Univ Lingayen Pangasinan Philippines

ISBN: (纸本)9783031733437;9783031733444

A brain tumor is an abnormal cell that grows in a certain region of the brain. the classification of tumors is usually conducted by experts in the medical field and manually performed by analyzing Brain MRI scans. this is usually a time-consuming task and prone to human error which can lead to improper diagnosis. In this study, a proposed hybrid CNN-Stacked LSTM model was developed and implemented to classify whether brain MRI scans contain tumors. the proposed approach achieved a classification accuracy of 94% in classifying brain MRI scans compared to the performance of the traditional CNN model which achieved a 92% classification accuracy. this shows that the proposed approach can be used to perform analysis on brain MRI scans and determine whether it contains a tumor. In addition, it was shown that combining deep learning models can be used to improve the performance of the traditional models in performing tasks such as classification. Furthermore, future work can be conducted to implement the proposed approach in performing multiclass classification and fine-tune hyperparameters to achieve optimal performance. Combining other existing deep learning models in performing image classification and performing parallelization can also be explored.

关键词： Brain Tumor Classification MRI Scans Convolutional Neural Network Long Short-Term Memory Hybrid Deep learning Approach

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2024 4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

2024 4th International Conference on Neural Networks, Inform...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

ISBN: (纸本)9798350394375

the proceedings contain 346 papers. the topics discussed include: dimensionality reduction methods comparison in the application of power prediction for a hybrid electrical vehicle;a deep neural networks-based significant wave height inversion method for GNSS-R signals;uncertainty-aware robot control via model-based reinforcement learning;research on image pre-processing techniques for multimode data metrology detection;characterization of signal transmission in bidirectional power frequency communication;joint trajectory and power optimization for UAV relay network over time-varying Rician channel;research and simulation of satellite communication channel for ship rolling in sea waves;an enhancement method with autoencoder for deep learning based hybrid beamforming;resource allocation method for 5G communication system based on ICHOA;and cross-domain recommendation based on meta-networks and attention transfer.

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Accessing the Power of Machine learning to Detect Fake News 4

Accessing the Power of Machine Learning to Detect Fake News

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4th intelligent Cybersecurity conference, ICSC 2024

作者： Ahmed, Imen Ben Magalhaes, Joao Paulo University of Sousse Sousse Tunisia ESTG Polytechnic of Porto CIICESI Rua do Curral Felgueiras Portugal

ISBN: (纸本)9798350354775

Global studies indicate that fake news spreads up to 70% faster than true news on social media, with significant consequences for society, including misinformation and political polarization. the problem of fake news can be addressed through a multifaceted approach (e.g. digital education and literacy, fact-checking, promotion of quality journalism, use of Artificial Intelligence, awareness, and public engagement). this paper explores the application of Machine learning algorithms to detect fake news. A dataset composed of 33,800 records and 216 features, created from fake and true news, is analyzed using several Machine learning models, such as neural networks, XGB, SVM, and Random Forest, comparing their effectiveness in classifying true and fake news. Feature selection techniques, such as SelectKBest and RandomForestClassifier, are used to optimize the performance of the models. the study also addresses the use of Explainable AI (XAI) methods, such as SHAP and LIME, to interpret model decisions, ensuring greater transparency and confidence in the proposed solutions. the results indicate that integrating Machine learning with explanatory approaches can provide powerful and reliable tools for identifying fake news. Within the scope of the same, accuracy rates of 99% were obtained, reinforcing the added value of the dataset and ML algorithms to provide reliable results for the identification of fake news, significantly contributing to the preservation of the integrity of the information. © 2024 IEEE.

关键词： Explainable Artificial Intelligence Fake News Machine learning

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Evaluating Pareto Solutions in optimization Problems of thermal Systems Using BHARAT Method 1

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4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Rao, Ravipudi Venkata Taler, Jan Taler, Dawid Rerak, Monika Sardar Vallabhbhai Natl Inst Technol Surat India Cracow Univ Technol Krakow Poland

ISBN: (数字)9789819746545

ISBN: (纸本)9789819796618;9789819746545;9789819746538

the BHARAT method is a simple and effective multi-attribute decision-making technique and is used in this work to assess the Pareto optimal solutions found during the optimal design of thermal systems. the method is utilized to determine the Pareto optimal compromise solution. the objectives are ordered and weighted according to how important they are for the current optimization problem. the weights and normalized values of the objectives are used to get the overall scores for each Pareto optimum solution. Two case studies are provided to make the proposed method understandable. In the first case study, an optimization problem with six objectives and thirty possible solutions for a DI diesel engine is studied. the second case study examines multi-objective optimization problem of a Stirling heat pump with three objectives and twenty-five possible solutions. Applying the proposed BHARAT approach to evaluate the Pareto solutions in the optimization problems of thermal systems is simple and convenient.

关键词： optimization thermal systems DI diesel engine Heat pump BHARAT

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4th International conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

4th International Conference on Smart IoT Systems: Innovatio...

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4th International conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

ISBN: (纸本)9789819751990

the proceedings contain 40 papers. the special focus in this conference is on Smart IoT Systems: Innovations and Computing. the topics include: Systematic Literature Review on Role of AI in IoT-Based Healthcare Solutions;a Hybrid Machine learning Model for Lung Disease Prediction;analyzing Student Feedback with Natural Language Processing for Enhancing Student Perception;enabling Technology for Smart and intelligent Transportation: Analyse the Uses and Impact of Blockchain Technology and Artificial intelligent on Trucking Industry for Better Results;Detecting Parkinson Disease Using Various AI Enabled Methods: Recent Advances, Requirements, and Open Challenges;synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security;detecting Inattentive and Aggressive Driver Behavior Using Deep learning: Recent Advances, Challenges with Performance Evaluation;Improved Brain Tumor Classification Accuracy Using CNN Architecture with Efficient Net on Magnetic Resonance Imaging;noise Prediction: A Case Study of Gjilan City;economic Crimes in Current Era: World and Indian Context;analyzing the Citation Networks Using Community Detection Approaches: A Review;A Hybrid Correlation Feature Selection Based Support Vector Machine (CFS-SVM) for Effective Software Defect Prediction;mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System;multimodal Data Fusion: Combining Big Data and Deep learning for Enhanced Predictive Models;Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack;preserving Our Heritage: Buildings Deep learning Solutions for Monitoring Cultural Heritage Structures Using Automated Crack Detection;Insurance_4_YOU: MCDM Equipped User-Friendly intelligent System for Personalized Life Insurance Recommendations;apriori Analysis of Deep learning Models on a Multi-class Data Set.

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Proceedings - 4th IEEE 2023 International conference on Computing, Communication, and intelligent Systems, ICCCIS 2023

Proceedings - 4th IEEE 2023 International Conference on Comp...

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4th IEEE International conference on Computing, Communication, and intelligent Systems, ICCCIS 2023

ISBN: (纸本)9798350306118

the proceedings contain 194 papers. the topics discussed include: comparative study of DDoS detection and mitigation techniques;a machine learning-based blockchain model for the storage of maternal health records and safety prediction;enhancing digital investigation: leveraging ChatGPT for evidence identification and analysis in digital forensics;managing metadata in data warehouse for data quality and data stewardship in telecom industry – a compact survey;a review on detection and prevention of the DDoS attacks in the blockchain;analysis of face recognition technique: plastic surgery altered face;image classification using federated averaging algorithm;navigating the gray area: a three-label framework for uncovering uncertainty in fake news;and improvement in validation score with loss function for breast cancer detection using deep learning.

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