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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Mingyang Chen, Xinyang Shu, Yang Li, Xiucheng Guan, Weili Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Data Science and Engineering East China Normal University China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

Unsupervised domain adaptation excels in transferring knowledge from a labeled source domain to an unlabeled target domain, playing a critical role in time series applications. Existing time series domain adaptation methods either ignore frequency features or treat temporal and frequency features equally, which makes it challenging to fully exploit the advantages of both types of features. In this paper, we delve into transferability and discriminability, two crucial properties in transferable representation learning. It's insightful to note that frequency features are more discriminative within a specific domain, while temporal features show better transferability across domains. Based on the findings, we propose Adversarial CO-learning Networks (ACON), to enhance transferable representation learning through a collaborative learning manner in three aspects: (1) Considering the multi-periodicity in time series, multi-period frequency feature learning is proposed to enhance the discriminability of frequency features;(2) Temporal-frequency domain mutual learning is proposed to enhance the discriminability of temporal features in the source domain and improve the transferability of frequency features in the target domain;(3) Domain adversarial learning is conducted in the correlation subspaces of temporal-frequency features instead of original feature spaces to further enhance the transferability of both features. Extensive experiments conducted on a wide range of time series datasets and five common applications demonstrate the state-of-the-art performance of ACON. Code is available at https://***/mingyangliu1024/ACON. © 2024 Neural information processing systems foundation. All rights reserved.

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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping

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Multimedia Tools and Applications 2025年第9期84卷 6665-6686页

作者： Chen, Si-Sheng Chang, Chin-Chen Horng, Ji-Hwei School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fujian Fuzhou35030 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Electrical Engineering National Quemoy University Kinmen89250 Taiwan

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mapping

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Image Segmentation Survey: Classical and Deep Learning Methods 4

Image Segmentation Survey: Classical and Deep Learning Metho...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bachani, Viren Roychowdhury, Anish Dasgupta, Abhijit Ei-Alfy, Hazem BSc. Data Science SP Jain School of Global Management Sydney Australia SP Jain School of Global Management Data Science Dept. Sydney Australia SP Jain School of Global Management Data Science Dept. Mumbai India School of Computer Science University of Sydney Sydney Australia Alexandria University Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9798350395914

Image segmentation is a prerequisite to almost all computer vision applications. It enables the extraction of meaningful information from visual inputs by partitioning images into segments with shared features. This review paper begins by outlining different image segmentation tasks. It then surveys a broad spectrum of segmentation techniques, ranging from classical frameworks to deep-learning (DL) architectures. We perform this through a systematic literature analysis across a handful of applications as part of our methodology. A key contribution of our survey is that it elaborates on a few situations where classical approaches outperform DL-based methods in accuracy, along with the added advantage of computational efficiency. In some cases, these findings also provoke interest in revisiting classical methods, prompting strategies that leverage the strengths of both paradigms. © 2024 IEEE.

关键词： Markov processes

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Deep mining algorithm for traffic accident data on highways under severe weather conditions

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第Speical issue期1卷 47-58页

作者： Zhang, J.Y. Cao, Y. Jiangsu Youth Science and Technology Center Nanjing210000 China School of Computer Engineering Jiangsu Second Normal University Nanjing210000 China Jiangsu Province Engineering Research Center of Basic Education Big Data A PPlication Nanjing210013 China

Analyzing traffic accident data is crucial for pinpointing contributing factors’ forecasting accident patterns’ and informing effective safety measures. This insight leads to enhanced road safety’ decreased fatalities’ and better resource allocation in transportation planning. Severe weather conditions’ including rain’ fog’ and sandstorms’ can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly’ collect traffic accident data under adverse weather conditions and complete attribute reduction’ denoising’ and normalization processing. Then’ by improving the decision tree algorithm’ the attribute selection criteria of the decision tree algorithm were enhanced;Finally’ the improved decision tree algorithm is aPPlied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%’ an accuracy rate of over 94.3%’ a recall rate of over 92.7%’ and a stable F1 value of around 0.8’ demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision suPPort for traffic management departments. © 2024, Aracne Editrice. All rights reserved.

关键词： data mining

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Sign Language to Text Conversion using RNN-LSTM

Sign Language to Text Conversion using RNN-LSTM

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Seviappan, Amutha Ganesan, Kothai Anbumozhi, Anna Reddy, A. Sameer Krishna, B. Vamsi Reddy, D.Sampath Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Coimbatore India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350348910

The speech-impaired community only uses sign language;the rest of society interacts verbally. Our research intends to fill this communication gap by proposing a state-of-the-art method for comprehending both static and moving signs in Indian Sign Language and translating them into text. data is collected, pre-processed, and hand recognition is finished using media pipe holistic before being categorized into suitable speech output. Because LSTM networks can develop long-term dependencies, they were investigated and employed for the classification of gesture data. The constructed model exhibited a 100% accuracy rate while categorizing 26 motions, highlighting the usefulness of LSTM-based neural networks for sign language translation. For those who are deaf or dumb, translating sign language into text allows them to interact with each other or with people in the general public by using hand gestures. According to a survey we conducted on sign language comprehension, most people are unable to identify the hand gestures and mode of communication used by sign language users. After hearing about all the difficulties, they have communicating with regular people, we developed this initiative on sign language transcription. The community of deaf and mute people will benefit from this initiative because it explains the meaning of each hand gesture and uses hand gestures to communicate various signs. In this project, hand gestures were recognized using a pipeline method called media pipe, and training and testing were carried out using deep learning techniques. Sign language was then converted to text format. © 2023 IEEE.

关键词： Audition

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Interactive Blood Bank Repository: Django AWS Approach 5

Interactive Blood Bank Repository: Django AWS Approach

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickavasagan, V. Lakshminarayanan, S. Ani Minisha, R. Rekha, K.S. Loganathan, V. Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Department of Computer Science and Business Systems Sri Sairam Engineering College Tamil Nadu India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu India Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr Rr and Dr Sr Engineering College Tamil Nadu India

ISBN: (纸本)9798350385649

Everyday, there are a growing number of people who require blood transfusions and there is no substitute for human blood. The secure blood donors are voluntary non-remunerated blood donors from risk populations. Donors in India and across the globe are three types namely, Voluntary donors, Professional donors and replacement donors. The deadline-driven web framework that supports Django's clean, practical design and speedy development. The existing system has a manual work restriction. This has restrictions on securely controlling the task for timely results declaration, and has a significant resource consumption. Web-based blood donation is the coordination of the supply and demand of blood through an online portal. The main goal of this research study is to give blood donors and blood searchers an online platform and to develop an engaging method of connecting the two groups. The suggested system provides a great deal of information regarding blood types, blood supply, and blood donation specifics to raise awareness among the public. Additionally, other system features like password-based security protection, report generation, chat functionality, and stock shortages can even improve the management tasks by streamlining them. © 2024 IEEE.

关键词： Economics

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Investigating the Utility of data Mining for Automated Credit Scoring 15

Investigating the Utility of Data Mining for Automated Credi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Dhyan Garima Soni, Rudransh Anandhasilambarasan, D. Sasirekha, R. Prema, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

data mining is a powerful device for automatic credit scoring. This paper will look into the application of statistics mining for automated credit score scoring by discussing its advantages and downsides. The form may even look at cutting-edge research in the location and discuss the demanding situations that information mining techniques pose while used for automatic credit score scoring. Similarly, the paper will provide pointers on how statistics mining techniques can improve the effectiveness of computerized credit score scoring. Subsequently, a precis of the paper's conclusions and implications for future studies could be supplied. © 2024 IEEE.

关键词： Conclusions Implications Improve Information Scoring

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Evaluating the Effectiveness of Deep Learning Models in Network Intrusion Detection

Evaluating the Effectiveness of Deep Learning Models in Netw...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Gaurav Gupta, Pankaj Yadav, Ganesh Kumar Verma, Richa Bhati, Jai Prakash Bhakuni, Vihan Singh IIMT College of Engineering Department of CSE Uttar Pradesh Greater Noida India Manager Data Engineer Discover Financial Services United States JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India Computer Science and Engineering BBDITM Lucknow India Greater Noida India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542344

Network security is now a significant concern in the current digital era since the increasing volume of cyber-attacks in day-to-day life has become a potential threat for the integrity and privacy of data. Most traditional intrusion detection systems face problems when it comes to detecting new and sophisticated attacks. The paper goes ahead to elaborate on how the concepts of machine learning and deep learning can be utilized to enhance the process of detecting and classifying network attacks. For instance, we experimented on the UNSW-NB15 dataset with different models: Decision Tree, Random Forest, SVM, KNN, MLP, and DNN. We have proved through our results that such kinds of models can considerably enhance the accuracy and robustness of IDS. With an average accuracy of 94.91%, the Decision Tree model took second place as the Deep Neural Network was closely followed. We will outline performance metrics based on the respective strengths and weaknesses of the models. This research is used to construct more effective and reliable network intrusion detection systems, which lead further into advancements of cybersecurity. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Mathematical Expressions in Software engineering Artifacts 23

Mathematical Expressions in Software Engineering Artifacts

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6th ACM India Joint International Conference on data science and Management of data, CODS-COMAD 2023

作者： Sur, Tanuj Jaisswal, Aaditree Vinayakarao, Venkatesh Data Science Chennai Mathematical Institute India Computer Engineering MKSSS's Cummins College of Engineering for Women Pune India

ISBN: (纸本)9781450397988

Mathematical expressions are required not only for numerical calculations but also for discussions and documentation. They play a significant role in providing clarifications and disambiguation of ideas, concepts and definitions. Software engineering artifacts such as source code, documentation and bug reports have mathematical expressions in them. Our experiments, with a commit message generation tool, suggest that mathematical expressions present in the input data affect the tool's accuracy. To help future research in this direction, we have constructed and shared a dataset of bug reports with both automated and manually annotated mathematical expressions. We have also shared a tool () to identify mathematical expressions. Our data set contains 2,040,120 bug reports from 10 different projects. We have used our tool to annotate all of these bug reports. From each project, we have also manually annotated 1000 bug reports. We annotated the dataset with the objective of developing toolkits to identify mathematical expressions in software engineering artifacts. Finally, we have made a case for future work to deal with mathematical expressions when improving software engineering tasks, especially those that use bug reports. © 2023 ACM.

关键词： Software engineering

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