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Advances in Transportation Studies 2024年 64卷 25-40页

作者： Liu, Y.P. Liu, B.N. Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan030008 China Department of Traffic Equipment and Control Engineering School of Traffic Electrical and Control Engineering North China University of Technology Beijing100043 China

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: Mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. © 2024, Aracne Editrice. All rights reserved.

关键词： Mean square error

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Analysis of Software Trustworthiness Based on FAHP-CRITIC Method

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 588-600页

作者：高晓彤马艳芳周伟 School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000AnhuiChina School of Computer Science and Information Engineering Changzhou Institute of TechnologyChangzhou 213000JiangsuChina

Software trustworthiness includes many *** weight allocation of trustworthy at-tributes plays a key role in the software trustworthiness *** practical application,attribute weight usually comes from experts'evaluation to attributes and hidden information derived from ***,when the weight of attributes is researched,it is necessary to consider weight from subjective and objective ***,a novel weight allocation method is proposed by combining the fuzzy analytical hierarchy process(FAHP)method and the criteria importance though intercrieria correlation(CRITIC)***,based on the weight allocation method,the trustworthiness measurement models of component-based software are estab-lished according to the seven combination structures of ***,the model reasonability is verified via proving some metric ***,a case is carried *** to the comparison with other models,the result shows that the model has the advantage of utilizing hidden information fully and analyzing the com-bination of components *** is an important guide for measuring the trustworthiness measurement of component-based software.

关键词： trustworthiness weight component-based software measurement

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A Systematic Analysis of Multi-Property Prediction Using Deep Learning in the Field of Drug Development

A Systematic Analysis of Multi-Property Prediction Using Dee...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Uriti, Archana Sethy, Abhisek Yalla, Surya Prakash GMR Institute of Technology Rajam Department of Information Technology AP Srikakulam India Silicon Institute of Technology Department of Computer Science and Engineering Odisha India MVGR College of Engineering Department of Computer Science and Engineering AP Vizianagaram India

ISBN: (纸本)9798350369175

In the rapidly changing field of pharmaceutical development, it is crucial to speed up clinical trials in order to introduce effective drugs in a timely manner. Conventional methods of forecasting molecular characteristics are often slow and time consuming, causing difficulties in the drug discovery process. However, Advancements in deep learning have streamlined pharmaceutical testing, enabling precise prediction of key drug properties like solubility, toxicity, reactivity, and bioactivity before synthesis. By employing different Simplified Molecular Input Line Entry System (SMILES) representations and training them with various deep learning algorithms like Graph Neural Networks (GNN) and Graph Convolution Networks (GCN). In this, a comparative study done on various technologies used in predicting molecular properties that helps to develop faster clinical trials in pharmaceutical industry. By identifying the limitations in the existing technologies, proposed an improved GCN which helps in better estimation of molecular properties. © 2024 IEEE.

关键词： Drug discovery

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Realization of Facial Recognition technology for Attendance Monitoring Through Biometric Modalities Employing MTCNN Integration

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SN computer Science 2024年第7期5卷 862页

作者： Gaur, Sachin Pandey, Milind Himanshu Department of Computer Science and Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Dwarahat India

The use of facial recognition technology in attendance tracking has become increasingly popular in a variety of industries. Conventional techniques, including radio frequency identification devices, have proved to be expensive, time-consuming, and unreliable. Researchers have looked to facial recognition for more effective attendance tracking in order to overcome these drawbacks. This paper proposes a novel method that merges facial recognition using K-nearest neighbours (KNN) and multi-task cascaded convolutional networks (MTCNN) via a Flask-based web application. The system's goal is to increase accuracy, especially in situations where masks partially conceal faces. Importing the required libraries and configuring a Flask application are part of the implementation. To improve context, pictures containing dates are taken when a user registers. MTCNN is used to address localization issues and increase the accuracy of face detection. The system makes sure that directories are managed correctly, which includes making folders for daily attendance records. Tracking user registration, extracting facial features with HaarCascade, identifying faces using a trained KNN model, and training the KNN model with readily available facial data are some of the main features. MTCNN demonstrates a considerable improvement by significantly increasing recognition accuracy. This combined system exemplifies the collaboration capabilities of Flask, MTCNN, HaarCascade, and KNN models by showcasing the real-world implementation of sophisticated convolutional networks in an intuitive interface. Experimental findings validate the system's efficacy in well-lit, unobstructed surroundings, with a recognition accuracy of 92.59–97.36% under ideal conditions. The present study highlights the smooth integration of different technologies, providing an adaptable framework for effective attendance control. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attendance monitoring Biometric modalities Facial recognition KNN Model MTCNN integration

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Predictive Vigilance: AI And ML Methods For DDOS Attack Detection And Forecasting 2

Predictive Vigilance: AI And ML Methods For DDOS Attack Dete...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ravikumar, S. Rajendran, P. Revathi, S. Kumar, K. Antony Saranraj, S. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Cmr Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India Kongunadu College of Engineering and Technology Department of Computer Science & Engineering Trichy India

ISBN: (纸本)9798350388916

Distributed network attacks are referred to, usually, as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. They used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, the UNWS-np-15 dataset was extracted from the GitHub repository and Python was used as a simulator. After applying the machine learning models, the generated a confusion matrix for identification of the model performance. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for the Random Forest algorithm. The average Accuracy (AC) of the proposed model is 89% which is superb and enough good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are approximately 90% for the XGBoost algorithm. The average Accuracy (AC) of the suggested model is 90%. By comparing present work to the existing research works, the accuracy of the defect determination was significantly improved which is approximately 85% and 79%, respectively. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

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computers, Materials & Continua 2023年第1期74卷 1069-1091页

作者： Rashid Jahangir Muhammad Asif Nauman Roobaea Alroobaea Jasem Almotiri Muhammad Mohsin Malik Sabah M.Alzahrani Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science University of Engineering and Technology LahorePakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and *** research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation *** this research,the performance of transformer and convolutional neural networks(CNN)are *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,***,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio *** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced *** UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 *** experimental results reveal that the proposed technique can achieve the best performance for ESC problems.

关键词： Environmental sound classification convolutional neural network deep learning transformer data augmentation

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Plant Disease Detection and Classification Using Hybrid Model Based on Convolutional Auto Encoder and Convolutional Neural Network

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computers, Materials & Continua 2025年第6期83卷 5219-5234页

作者： Tajinder Kumar Sarbjit Kaur Purushottam Sharma Ankita Chhikara Xiaochun Cheng Sachin Lalar Vikram Verma Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaurYamunanagar135133India Department of Computer Science Government PG CollegeAmbala CanttAmbala134003India School of Computer Science&Engineering Galgotias UniversityGreater Noida203201India Department of Computer Science and Applications Kurukshetra UniversityKurukshetra136118India Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Department of CSE Panipat Institute of Engineering and TechnologyPanipat132103India

During its growth stage,the plant is exposed to various *** and early detection of crop diseases is amajor challenge in the horticulture *** infections can harmtotal crop yield and reduce farmers’income if not identified ***’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant *** is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of *** alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without *** automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of *** date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves *** trained on a dataset obtained from the Plant Village *** dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training ***,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.

关键词： Tomato leaf disease deep learning DenseNet-121 convolutional autoencoder convolutional neural network

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Hybrid Filtering-Based Product Recommendation System Integrating GRU and BFGS Optimization

Hybrid Filtering-Based Product Recommendation System Integra...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Suresh, A. Kumar, R.G. Nagaraju, D. Mohana Sundaram, K.D. Anandan, B. Siddharth Institute of Engineering and Technology Department of Computer Science and Engineering Puttur India Siddharth Institute of Engineering and Technology Department of ECE Puttur India

ISBN: (纸本)9798331522988

It is more crucial than ever to handle crucial problems including data sparsity, cold-start problems, and the requirement for extremely precise forecasts in today's ever changing recommendation system field. This work presents a hybrid filtering product recommendation system that integrates Gated Recurrent Units (GRU) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method to significantly enhance recommendation accuracy and performance. Standard techniques such content-based filtering and collaborative filtering can fail to offer reliable recommendations for large and dynamic datasets. Our hybrid model uses BFGS optimization to increase model accuracy and efficiency while utilizing GRU-based collaborative filtering to capture sequential user-item interactions. The GRU-BFGS model outperforms matrix factorization (MF) techniques in numerous areas, achieving an astounding accuracy of up to 92% on the Amazon customer review dataset. It also exhibits excellent recall, precision, and notable decreases in error metrics (MAE, MSE, and RMSE), which makes it a very successful method for providing individualized, accurate suggestions. © 2024 IEEE.

关键词： Collaborative filtering

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Single Sample Face and Ear Recognition using Transfer Learning and Sample Expansion

Single Sample Face and Ear Recognition using Transfer Learni...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Tomar, Vivek Kumar, Nitin Deshmukh, Maroti Singh, Maheep National Institute of Technology Computer Science and Engineering Department Garhwal Uttarakhand Srinagar India Computer Science and Engineering Department Chandigarh India Doon University Computer Science Department Dehradun India

ISBN: (纸本)9798350388534

Single-sample face and ear recognition is a challenging problem in biometrics due to the fact that only a single face or ear image sample is available for the training. However, during the recognition, test images may have possible variations like different poses, occlusion, brightness, etc, which increase the difficulty of the problem. Deep learning approaches have attracted researchers working in this area. However, the selection of proper methods for feature extraction and populating the training set for deep learning methods is still a challenge in this field. In this article, a method to solve the single-sample face and ear recognition problem is proposed that combines transfer learning with sample expansion to increase recognition accuracy. The proposed method uses sample expansion by intensity variation in random pixels before feeding the images to the VGG16 deep learning model with pre-trained ResNet50 weights on the ImageNet database for feature extraction. The proposed method is tested on two popular databases, ORL face and AMI ear databases. Accuracy results prove the effectiveness of the proposed method as it outperforms the conventional principal component analysis (PCA) feature extraction method by 26.51% and 46.47% in terms of classification accuracy on the ORL face and AMI ear database. This strengthens the assumption that deep learning models with appropriate sample expansion can be useful even in scenarios where we have smaller databases with limited or single training samples. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Impact of Financial News Sentiment on Stock Price Forecasting: A Comparative Deep Learning Approach 1

Exploring the Impact of Financial News Sentiment on Stock Pr...

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1st IEEE International Conference on Communications and computer Science, InCCCS 2024

作者： Rajhans, Abhishek Das, Trinanjan Kumar, Anish Sinha, Bam Bahadur Computer Science and Engineering Indian Institute of Information Technology Ranchi India Computer Science and Engineering National Institute of Technology Sikkim India

ISBN: (纸本)9798350358858

The application of Artificial Intelligence (AI) in stock price prediction has demonstrated significant advancements, with Machine Learning and Deep Learning techniques proving highly efficient in this domain. Two widely adopted architectures for stock price prediction are the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. Recognizing the potential impact of financial news sentiment on forecasting, this study investigates whether incorporating such sentiment yields superior results compared to relying solely on historical stock prices. Accurate predictions in the financial market are challenging due to its inherent high volatility and non-linear nature. To contribute insights into the effectiveness of various deep learning architectures, a comparative analysis was conducted on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and the Prophet model. The evaluation focused on three companies listed on the National Stock Exchange (NSE). The study employs the Mean Squared Error (MSE) metric to rigorously assess and compare the models’ performance. The results of the comparative analysis provide valuable implications for precision in stock price prediction within the context of market volatility and non-linearity. The study adds to the growing body of research on the application of AI in financial forecasting, emphasizing the importance of considering sentiment analysis alongside traditional approaches for enhanced predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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