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arXiv 2024年

作者： Zhang, Yuling Wu, Anpeng Kuang, Kun Du, Liang Sun, Zixun Wang, Zhi Tsinghua Shenzhen International Graduate School Tsinghua University China Tsinghua-Berkeley Shenzhen Institute Tsinghua University China College of Computer Science and Technology Zhejiang University China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Interactive Entertainment Group Tencent

Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods. Copyright © 2024, The Authors. All rights reserved.

关键词： machine learning

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Reversible adversarial information hiding based on the interpretation of neural network

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Digital Signal Processing: A Review Journal 2025年 165卷

作者： Li, Ruiping Wang, Xi Cui, Qingchen Li, Ming Information Construction and Management Office Henan Normal University Xinxiang453007 China School of Artificial Intelligence and Software Engineering Nanyang Normal University Nanyang473061 China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Henan Nanyang473000 China College of Information Engineering Zhoukou Vocational College of Arts and Science Zhoukou466000 China College of Computer and Information Engineering Henan Normal University Xinxiang453007 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang453007 China

The emerging technology of adversarial information hiding can generate adversarial example by embedding useful information instead of meaningless noise into the host image. The obtained image can deceive classification models and therefore equip the function of privacy protection. However, existing research mainly focuses on the effectiveness of attack rather than preserving the integrity of the protected image. It cannot meet the requirements in the fields such as medical imaging, financial transactions, and copyright protection where users' raw data should not be damaged. Therefore, we propose a novel reversible adversarial information hiding based on interpretability of neural networks. The Grad-CAM-generated heatmap is employed to identify a minimal set of high-impact pixels for embedding, ensuring that minor modifications can induce significant misclassification. Then, the user-defined secret bits are embedded into the identified pixels by using difference expansion. After extraction, the original image can be perfectly restored. The technologies of adversarial example and reversible information hiding are combined to accommodate wider applications. The proposed method is tested on CIFAR-10 with three different neural architectures (NiN, AlexNet, ResNet). Experimental results show that the proposed method can completely restore the cover image while ensuring the extraction of the embedded data and misleading the neural network. © 2025 Elsevier Inc.

关键词： Adversarial attacks Adversarial information hiding Neural network interpretability Reversibility

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Bangladeshi Traffic Sign Recognition and Classification using CNN with Different Kinds of Transfer learning through a new (BTSRB) Dataset

Bangladeshi Traffic Sign Recognition and Classification usin...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Md. Abu Sayeed Md. Saiful Islam Md Babul Islam Piyush Kumar Pareek Tanbin Islam Rohan Electrnoics and Telecommunication Engineering Chittagong University of Engineering & Technology Chattogram Bangladesh School of ICT DIMES Lab University of Calabria Rende Italy Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Bahari Data Research House Dhaka Bangladesh

An accident is the cry of a lifetime. The role of traffic signs is most important in preventing or mitigating accidents. In many cases, while driving, traffic signs are not visible in the conventional approach; if traffic sign detection and recognition can alert the driver of important sign guidance ahead, it will help in accident mitigation. Traffic sign recognition plays a monumental role in expert systems, such as traffic assistance driving systems and automatic driving systems. The prime purpose of this paper is to design and identify a computer-based system that can spontaneously detect the direction of a road sign. For this research work, we have created our own dataset, which is called the Bangladeshi Traffic Sign Recognition Benchmark (BTSRB) dataset. The dataset, BTSRB, was created by capturing images from different angles and under different parameters and conditions. A total of 7320 images were collected to create this comprehensive database. This dataset called BTSRB all the images collected from Bangladesh. In this paper, we used five different types of models (CNN, Inception V3, MobileNetV2, ResNet50, and VGG16), which are pre-trained on the ImageNet dataset. Later, we finetuned the pre-trained model and used transfer learning. The main challenge of this research is collecting datasets from a country like Bangladesh, where no recognized dataset is available. When compared to another model, the accuracy of this model is greater than 91%. This paper emphasizes the significance of traffic sign recognition in expert systems and the necessity for a well-established dataset in nations where such resources are not readily available.

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Rotation-Invariant Self-Supervised Representation learning for Object Detection in Autonomous Driving

Rotation-Invariant Self-Supervised Representation Learning f...

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Computational intelligence and Network Systems (CINS), International Conference on

作者： Aboli Marathe Sidharth Roy Devansh Singh Rahee Walambe Ketan Kotecha Machine Learning Dept. Carnegie Mellon University Pittsburgh USA Dept. of Computer and Information Science University of Pennsylvania Philadelphia USA Dept. of AIML Engineering Symbiosis Institute of Technology (SIT) Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

This paper describes a self supervised representation learning approach that can perform robust object detection in out-of-distribution rotated images for autonomous driving task. Keeping in mind the limitations of vehicle sensors we incorporate self-supervised rotation invariance for correcting rotational corruptions. We present a comprehensive analysis of observed label shifts, validate the results using holdout sets and provide explainability for the performance uisng GradCAM appaorach. We demonstrate results on real-world adversarial examples: (Autonomous Scenes - DAWN dataset) that signifi-cantly outperform common methods. The proposed framework outperforms the rotation-affected benchmark by 14.73 points of mAP for the OD task in autonomous driving applications.

关键词： Representation learning Three-dimensional displays Fitting Object detection Self-supervised learning Benchmark testing Sensors Autonomous vehicles Meteorology Network systems

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Convolutional Neural Network Based Classification of WeChat Mini-Apps

Convolutional Neural Network Based Classification of WeChat ...

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IEEE International Conference on Communications (ICC)

作者： Yihao Jin Xuanyu Liu Xiao Fu Bin Luo Xiaojiang Du Mohsen Guizani State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

In recent years, a novel mobile computing paradigm has been evolving rapidly, with a host app allowing users to install and run mini-apps inside the app itself. However, the current classification mechanism of mini-apps is blurry and coarse-grained, making users unable to clearly understand mini-app functions, which can result in a series of privacy issues. In this study, an automatic convolutional neural network (CNN)-based classification approach is proposed for Wechatmini-apps. The proposed method integrates the static and dynamic features of WeChat mini-apps to achieve precise classification. Our approach was evaluated in a real-world testbed and the results showed that it can effectively classify Wechatmini-apps into proper categories, helping users better understand the functions of WeChat mini-apps while reducing user privacy violations.

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Optimizing Energy Management and Load Balancing Through AI-Driven Quantum Approximate Optimization

Optimizing Energy Management and Load Balancing Through AI-D...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Manjunath Kamath K Manasa M Anitha B. S Mahadevi Falguni Tlajiya Sangramjit Chavan Department of Cyber Security NMAM Institute of Technology NITTE (Deemed to be University) Karkala India Department of Artificial Intelligence and Machine Learning Brindavan College of Engineering Bengaluru India Department of Computer Science Sarada Vilas College Mysuru India Department of Electrical Engineering Parul institute of Engineering and Technology Parul University Gujarat India Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The integration of Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) offers a promising approach to addressing energy management and load balancing challenges in modern power systems. This study explores the application of these quantum computing techniques, coupled with artificial intelligence (AI), to optimize energy distribution across smart grids. The proposed hybrid model leverages the problem-solving capabilities of QAOA for discrete optimization and the real-time adaptability of QA for minimizing energy consumption and operational costs. AI techniques are employed to predict energy demand, balance loads, and ensure efficient resource allocation. Simulations were conducted on a smart grid scenario involving renewable energy sources, varying demand, and distributed energy generation. The results demonstrate that the hybrid model achieved a 25% improvement in load balancing efficiency and a 30% reduction in energy wastage compared to traditional optimization methods. Additionally, the model reduced peak energy demand by 20%, contributing to overall grid stability. The quantum-enhanced approach also decreased computational complexity by 40%, enabling faster decision-making in dynamic environments. These findings highlight the potential of combining quantum algorithms with AI for sustainable and efficient energy management.

关键词： Data centers Renewable energy sources Quantum algorithm Computational modeling Power system stability Load management Quantum annealing artificial intelligence Optimization Load modeling

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Cognitive Computing in Intelligent Traffic Management Systems

Cognitive Computing in Intelligent Traffic Management System...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： A V V Sudhakar Anuj Tyagi Pallavi Sachin Patil K. Suresh Kumar Manoj Sathe B. T. Geetha Department of EEE School of Engineering SR University Warangal Telangana India Northeastern University USA Department of Artificial Intelligence & Machine Learning Genba Sopanrao Moze College of Engineering Balewadi Savitribai Phule Pune University Pune MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai Computer Science PES Modern Institute of Business Studies Pune India Department of ECE Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Intelligent business operation systems are undergoing a revolution as a result of cognitive computing, which is boosting their capacity to analyze large amounts of data and produce well-informed conclusions in real-time. The purpose of this investigation is to investigate the possibility of incorporating cognitive computing technologies, such as machine literacy, natural language processing, and big data analytics, into business operation systems to address the issue of civic traffic, improve the flow of business, and enhance safety. The development of adaptive business control mechanisms that can respond robustly to changing business situations is made possible by cognitive computing. This is accomplished through the use of sophisticated algorithms and predictive analytics. The purpose of this article is to investigate case studies and real-world implementations, to highlight the major improvements in company efficiency and decrease in travel times that may be realized using cognitive computing. The findings provide evidence that cognitive computing has the potential to transform conventional business operation systems into intelligent, responsive networks that are capable of tone-optimization. This would result in the creation of civic environments that are more intelligent and more environmentally sustainable.

关键词： Operating systems Transforms Prediction algorithms Real-time systems Natural language processing Safety Time factors

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machine learning model for predicting pancreatic fistula after pancreatoduodenectomy

Machine learning model for predicting pancreatic fistula aft...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Hager Saleh Nora El-Rashidy Eman Mohamed Ahmad M Sultan Ayman El Nakeeb Shaker El-Sappagh Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Faculty of Computers and Information Damnhour University Damnhour Egypt Gastroenterology Surgical Center Faculty of Medicine Mansoura University Mansoura Egypt Faculty of Computer Science and Engineering Galala University Suez Egypt

Pancreaticoduodenectomy (PD) is a complex surgery used mainly to treat tumors and other pancreas disorders. PD is considered one of the most challenging surgeries because it may have several complications, including bleeding and infections in the surgical area, temporary or permanent diabetes, and pancreatic leakage (PL), which may lead to morbidity and mortality. In this study, we build an accurate and medically oriented machine learning model that predicts PL after PD based on patient markers collected only before the PD operation. The study is made using a real-world dataset for 397 Egyptian patients. The proposed machine learning pipeline starts with a data preprocessing step that handles the missing data values by the median values. In the next step, diverse interpretable classifiers, including logistic regression, random forest, decision tree, support vector machine, XGBoost, and AdaBoost, are utilized to predict the PL. Hyperparameter optimization is done using grid search with k-fold cross-validation. The results indicate that XGBoost achieves the highest marks, outperforming the state-of-the-art techniques in several evaluation metrics (i.e., accuracy= 91%, precision= 90.96%, recall= 91.0%, F1-score= 90.97%, and AUC=89.78%. The resulting model is accurate enough to be medically relevant for PL prediction in real healthcare settings.

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An iterative-based difference scheme for nonlinear fractional integro-differential equations of Volterra type

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Saini, Gaurav Ghosh, Bappa Chand, Sunita Mohapatra, Jugal Center for Data Science Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Center for Artificial Intelligence and Machine Learning Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics National Institute of Technology Rourkela India

This paper presents an iterative difference scheme for solving nonlinear fractional integro-differential equations of Volterra type, which are widely used in modeling memory-dependent phenomena in various scientific and engineering applications. The scheme employs Caputo's fractional derivative to handle the non-local nature of the fractional derivative and introduces an iterative approach to enhance accuracy and stability. By discretizing the domain of interest on a uniform mesh and the present non-local differential operator using a well-established L1 technique, we derive a nonlinear difference equation that captures the essential dynamics of the underlying continuous problem. The corresponding nonlinear equation is solved by employing the Daftardar-Gejji and Jafari (DGJ) method. The convergence and stability of the proposed scheme are rigorously analyzed under sufficiently smooth initial data, and the method produces reliable numerical solutions. Numerical experiments are conducted to validate the effectiveness of the scheme, demonstrating its efficiency. © 2025 The Authors

关键词： Composite trapezoidal formula Error estimation L1 technique Nonlinear integro-differential equations

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Blockchain Enabled Secure Data Sharing in Medical Sensors Networks: Concise Review

Blockchain Enabled Secure Data Sharing in Medical Sensors Ne...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： David Raymond Praveen Kumar Palash Gourshettiwar Induni Werarathna Divyani Mankar Prateek Verma Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Sciences School of Health Allied Sciences Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Medical sensor networks have transformed patient monitoring and treatment approaches in the ever-changing healthcare industry. However, strong systems for safe and effective data sharing are necessary to fully use sensor data. Conventional methods of exchanging data in healthcare are frequently rife with security flaws, such as the potential for data tampering and privacy violations. Blockchain technology offers decentralized, irreversible, and transparent data sharing and storage capabilities and offers a promising solution to these problems. The potential for safe data sharing offered by Blockchain technology to revolutionize healthcare delivery is examined in this review paper, which examines the convergence of medical sensor networks and Blockchain technology. This paper explores the various Blockchain implementation architectures, approaches, and case studies in healthcare contexts by thoroughly examining current literature. This paper also discusses the prospects, difficulties, and future directions in this emerging subject, emphasizing the effects on interoperability, patient care, and data security. By explaining the revolutionary possibilities of secure data sharing in medical sensor networks afforded by Blockchain technology, the current study hopes to stimulate additional investigation and creativity in the quest for safer and more effective healthcare systems.

关键词： Industries Technological innovation Reviews Data security Scalability Medical services Transforms Blockchains Interoperability Research and development

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