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Smart food choices: the role of reliable and safe AI in nutrition recommender systems

Life Cycle Reliability and Safety engineering 2025年 1-11页

作者： Jayaswal, Ruchi Ansari, Mohd. Aquib Mewada, Arvind Department of AIML Symbiosis Institute of Technology Maharashtra Pune 412115 India School of Computer Science Engineering and Technology Bennett University UP Greater Noida 201310 India

Inappropriate food consumption can lead to various medical problems and illnesses. Individuals usually lean on drugs instead of taking preventative action in their food consumption due to a lack of sufficient knowledge about healthy nutrition. The impact of well-established applications on clinical outcomes is still not completely known. A major share of the worldwide population has already become overweight, which is resulting in a variety of ailments caused by bad eating habits, such as increasing consumption of high-energy and high-fat foods. Thus, to address this, a diet recommendation system will likely provide a better solution for people’s healthy eating habits. This study provides a unifying paradigm for food recommendation systems and discusses key challenges impacting them, such as incorporating different contexts and domain information. We examine existing solutions to these problems before elaborating on research constraints and future initiatives in this subject. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.

关键词： Caloric requirements Dietary suggestions Health and wellness Multi-sensor fusion Nutrition recommender systems Personalized meal preferences

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IPAD: Iterative, Parallel, and Diffusion-Based Network for Scene Text Recognition

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International Journal of computer Vision 2025年 1-21页

作者： Yang, Xiaomeng Qiao, Zhi Zhou, Yu VCIP & TMCC & DISSec College of Computer Science Nankai University Tianjin300350 China Institute of Information Engineering CAS Beijing100089 China Tomorrow Advancing Life Beijing100080 China

Nowadays, scene text recognition has attracted more and more attention due to its diverse applications. Most state-of-the-art methods adopt an encoder-decoder framework with the attention mechanism, autoregressively generating text from left to right. Despite the convincing performance, this sequential decoding strategy constrains the inference speed. Conversely, non-autoregressive models provide faster, simultaneous predictions but often sacrifice accuracy. Although utilizing an explicit language model can improve performance, it burdens the computational load. Besides, separating linguistic knowledge from vision information may harm the final prediction. In this paper, we propose an alternative solution that uses a parallel and iterative decoder that adopts an easy-first decoding strategy. Furthermore, we regard text recognition as an image-based conditional text generation task and utilize the discrete diffusion strategy, ensuring exhaustive exploration of bidirectional contextual information. Extensive experiments demonstrate that the proposed approach achieves superior results on the benchmark datasets, including both Chinese and English text images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Optical character recognition

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Fast Demodulation of OFDR-Based Distributed Sensing Based on Enhanced Buneman Frequency Estimation

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Journal of Lightwave Technology 2025年第9期43卷 4283-4289页

作者： Zhang, Zhaopeng Liu, Bo Liu, Xiao Li, Caiyun Alsalman, Osamah Zhu, Chen Zhejiang Laboratory Research Center for Novel Computing Sensing and Intelligent Processing Hangzhou311121 China King Saud University Department of Electrical Engineering College of Engineering Riyadh11421 Saudi Arabia Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process—typically required for a finer spectral sampling interval but at the cost of demodulation efficiency—can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional algorithms in terms of underlying physical principles and resultant demodulation noise distribution, establishing a strong foundation for adopting the BFE algorithm to improve demodulation efficiency without sacrificing performance metrics. Demonstration experiments using identical hardware configurations show that the proposed BFE method enhances demodulation efficiency by over 17 times without sacrificing other performance metrics, confirming its effectiveness and practicality. This advancement holds promise for improving the practical application of OFDR-based distributed sensing systems. © 1983-2012 IEEE.

关键词： Frequency domain analysis

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Osmotic Computing Based Secure Resource Orchestration Scheme for Vehicular Communication

Osmotic Computing Based Secure Resource Orchestration Scheme...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Deepanshu Garg Mohit Angurala Rasmeet S Bali Neeraj Kumar Computer Science & Engineering Department Chandigarh University India Computer Science Department Guru Nanak Dev University India Computer Science & Engineering Department Thapar University Patiala India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

The advancement in technology leads to provide an efficient communication among vehicles to offload resource-intensive tasks for transportation-based services. However, it may cause issue related to efficient secure resource utilization among vehicular nodes. The key reason behind these considerations are the high mobility of vehicles and their high computational requirements for task execution. To solve these issues, an osmotic computing based vehicular communication scheme has been proposed. The primary objective of the scheme is to provide a communication model which can perform effective secure resource management among static or dynamic vehicular nodes through Osmotic Computing. By utilizing the key components of osmotic computing such as osmotic resource manager and node manager, numerous resources have been shared with the vehicles in an effective way. The performance of the proposed scheme has been evaluated using simulation. Results obtained show that the proposed scheme outperform the existing schemes in terms of various performance evaluation parameters.

关键词： Performance evaluation Communication systems Computational modeling Data dissemination Decision making Dynamic scheduling Data models Resource management Reliability Vehicle dynamics

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GNAPing On the Job: Attacking and Defending Facial Detection on Edge Devices

GNAPing On the Job: Attacking and Defending Facial Detection...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Solanki, Abhijeet Thornton, Ryan Taylor Hasan, Syed Rafay Qidwai, Uvais Tennessee Technological University Department of Electrical and Computer Engineering CookevilleTN38505 United States Lander University Department of Mathematics and Computing GreenwoodSC29649 United States Qatar University Department of Computer Science and Engineering Qatar

ISBN: (纸本)9798331504847

Facial detection systems, particularly those deployed on edge devices, have become crucial components of modern security infrastructures. However, their susceptibility to adversarial attacks raises significant concerns, particularly when attackers exploit signal-dependent noise to target critical features like facial landmarks. This paper introduces the Guided-inspired Noise Attack Pyramid (GNAP), a novel adversarial attack model that leverages the Laplacian-of-Gaussian (LoG) and Laplacian Pyramid filters to reduce CNN-based facial recognition accuracy by targeting high-frequency regions of images. We evaluate GNAP's effectiveness through experiments on the Labeled Faces in the Wild (LFW) dataset and real-time video processing on edge devices, demonstrating up to an 18 percent reduction in classification confidence with minimal computational impact. To counter GNAP, we propose the Guided Noise Attack Guard (GNAG) defense strategy, utilizing an Unsharp Mask filter to restore classification confidence to near-original levels without sacrificing real-time performance. The combined insights offer a balanced approach to both attacking and defending facial recognition systems on resource-constrained edge devices. The code to reproduce our results is available at https://***/ChiefAj23/***. © 2025 IEEE.

关键词： Edge detection

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Fusion of quantum computing and explainable AI: A comprehensive survey on transformative healthcare solutions

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Information Fusion 2025年 122卷

作者： Singh, Shashank Sheshar Kumar, Sumit Ahuja, Rohit Barua, Jayendra Department of Computer Science and Engineering Thapar Institute of Engineering & Technology Patiala India

Rapid technological advancements in healthcare have significantly enhanced diagnostics and patient care. The exponential growth of complex medical data requires innovative architectures to ensure efficient processing and real-world deployment. To address this need, quantum computing is revolutionizing healthcare applications by efficiently processing high-dimensional data. At the same time, artificial intelligence (AI) enhances disease detection and treatment planning;however, its black-box nature reduces trust. Explainable artificial intelligence (XAI) bridges this gap by improving interpretability and accountability in healthcare applications. This study explores the fusion of quantum computing with XAI (QXAI) to support improved medical decision-making. It presents a comprehensive survey of state-of-the-art quantum-based, XAI-based, and QXAI-based techniques for healthcare solutions using a survey research questions-based methodology. We critically evaluate the capability of QXAI models to analyze vast and high-dimensional healthcare data using case studies. This work also highlights major challenges in the adoption of QXAI, such as privacy, ethical concerns, and standardization. Through a comprehensive analysis of recent developments, this review outlines key research directions to make QXAI more interpretable, reliable, and ethically compliant in real-world medical applications. Thus, this review provides valuable insights for researchers and offers practical guidelines for implementing quantum-enabled XAI models in real-world healthcare environments. © 2025 Elsevier B.V.

关键词： Personalized medicine

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 IEEE.

关键词： Convolutional Neural Networks Deep Learning Discrete Cosine Transform Embedding Steganography

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Predicting Customer Lifetime Value in E-commerce: A Data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

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An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests 3

An Enhanced Framework for Churn Prediction using Stratifed B...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Bhardwaj, Anay Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essential to identify the factors driving churn, enabling businesses to implement timely and effective retention strategies.A framework is proposed which combines stratified bagging with multi-output Random Forest and stacked feature transformation, to enhance prediction accuracy. This approach balances the dataset, trains a custom Random Forest using stratified bagging, and applies stacked learning using Logistic Regression on transformed features. Tested on real-world data, the model demonstrated 99% accuracy, showing improvements over the classic Random Forest in precision, recall, and prediction time. © 2025 IEEE.

关键词： Sales

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