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2024 Workshop on Electronics Communication Engineering, WECE 2024

作者： Pan, Aimin Abisado, Mideth B. Wang, Ruofan Wang, Nan College of Computing and Information Technologies National University ManilaPH 1008 Philippines School of Electronics and Information Engineering Wuhan Donghu University Wuhan430212 China Department of Electronics Xinzhou Normal University Xinzhou034000 China School of Computer Science Wuhan Donghu University Wuhan430212 China

ISBN: (数字)9781510689145

ISBN: (纸本)9781510689138

Depression is characterized by significant and persistent low mood, and is the main type of mood disorder, which seriously affects people's daily work and life. The researchers found that there were many significant differences in speech between normal people and people with depression. This paper presented an audio depression regression prediction model (DR AudioNet) based on convolutional neural networks and long short-term memory. Through research on feature extraction and network architecture, a multiscale audio delta normalization (MADN) feature extraction algorithm was proposed. MADN features described the characteristics of non-personalization speech, and two network models based on DR AudioNet optimization were designed according to the MADN feature of the two adjacent segments. Increase the sample length to obtain more training data without reducing the sample size. The proposed audio depression recognition algorithm, compared with the existing approaches, has improved the prediction accuracy in the diagnosis of depression. © 2025 SPIE.

关键词： Convolutional neural networks

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7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

7th International Workshop on Predictive Intelligence in Med...

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7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031745607

The proceedings contain 17 papers. The special focus in this conference is on Predictive Intelligence in Medicine. The topics include: Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive models for Neuroimaging;RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence;gene-to-Image: Decoding Brain Images from Genetics via Latent Diffusion models;physics-Guided Multi-view Graph neural network for Schizophrenia Classification via Structural-Functional Coupling;automated Patient-Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy;MNA-net: Multimodal Neuroimaging Attention-Based Architecture for Cognitive Decline Prediction;Improving Brain MRI Segmentation with Multi-Stage Deep Domain Unlearning;DynGNN: Dynamic Memory-Enhanced Generative GNNs for Predicting Temporal Brain Connectivity;Strongly Topology-Preserving GNNs for Brain Graph Super-Resolution;generative Hypergraph neural network for Multiview Brain Connectivity Fusion;identifying Brain Ageing Trajectories Using Variational Autoencoders with Regression Model in Neuroimaging Data Stratified by Sex and Validated Against Dementia-Related Risk Factors;integrating Deep Learning with Fundus and optical Coherence Tomography for Cardiovascular Disease Prediction;self-Supervised Contrastive Learning for Consistent Few-Shot Image Representations;Neurocognitive Latent Space Regularization for Multi-label Diagnosis from MRI;Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study.

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Brain-like training of a pre-sensor optical neural network with a backpropagation-free algorithm

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PHOTONICS RESEARCH 2025年第4期13卷 915-923页

作者： Huang, Zheng Wang, Conghe Zhang, Caihua Shi, Wanxin Wu, Shukai Yang, Sigang Chen, Hongwei Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China

Deep learning has rapidly advanced amidst the proliferation of large models, leading to challenges in computational resources and power consumption. optical neural networks (ONNs) offer a solution by shifting computation to optics, thereby leveraging the benefits of low power consumption, low latency, and high parallelism. The current training paradigm for ONNs primarily relies on backpropagation (BP). However, the reliance is incompatible with potential unknown processes within the system, which necessitates detailed knowledge and precise mathematical modeling of the optical process. In this paper, we present a pre-sensor multilayer ONN with nonlinear activation, utilizing a forward-forward algorithm to directly train both optical and digital parameters, which replaces the traditional backward pass with an additional forward pass. Our proposed nonlinear optical system demonstrates significant improvements in image classification accuracy, achieving a maximum enhancement of 9.0%. It also validates the efficacy of training parameters in the presence of unknown nonlinear components in the optical system. The proposed training method addresses the limitations of BP, paving the way for applications with a broader range of physical transformations in ONNs. (c) 2025 Chinese Laser Press

关键词： Deep learning neural networks optical computing optical neural systems optical systems Spatial light modulators

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Enhancing Odia HCR: Tracing, Zoning and neural networks 5

Enhancing Odia HCR: Tracing, Zoning and Neural Networks

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Mishra, Soumya Ranjan Pramanik, Subhadip Pathak, Debanjan Mohapatra, Hitesh University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798331522100

Recognition of handwritten characters remains a challenging task, especially across diverse languages and scripts. optical character recognition (OCR) plays a pivotal role in transforming images of handwritten documents into machine-readable text. Whether performed online (during documentation) or offline (from stored documents), OCR is utilized in a variety of fields, including medical transcription, language translations, the digitization of ancient manuscripts, and more. In this work, we propose an efficient framework for handwritten character recognition, applicable to both offline and online processes. Our approach involves: We preprocess handwritten character images to make them suitable for feature extraction. Instead of relying solely on pixel intensities, we extract global and local shape features. These features capture the essence of character shapes. We employ a Kohonen neural network (SOM) to learn the underlying patterns in handwritten characters. Our experiments on the NITRKL dataset demonstrate the superiority of our models. By understanding the strokes, medium, and veracity of the data, we achieve high-quality recognition results. © 2025 IEEE.

关键词： Self organizing maps

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SafeLight: Enhancing Security in optical Convolutional neural network Accelerators

SafeLight: Enhancing Security in Optical Convolutional Neura...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Salma Afifi Ishan Thakkar Sudeep Pasricha Department of Electrical and Computer Engineering Colorado State University Fort Collins USA Department of Electrical and Computer Engineering University of Kentucky Lexington USA

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapid proliferation of deep learning has revolutionized computing hardware, driving innovations to improve computationally expensive multiply-accumulate operations in deep neural networks. Among these innovations are integrated silicon-photonic systems that have emerged as energy-efficient platforms capable of achieving light speed computation and communication, positioning optical neural network (ONN) platforms as a transformative technology for accelerating deep learning models such as convolutional neural networks (CNNs). However, the increasing complexity of optical hardware introduces new vulnerabilities, notably the risk of hardware trojan (HT) attacks. Despite the growing interest in ONN platforms, little attention has been given to how HT-induced threats can compromise performance and security. This paper presents an in-depth analysis of the impact of such attacks on the performance of CNN models accelerated by ONN accelerators. Specifically, we show how HTs can compromise microring resonators (MRs) in a state-of-the-art non-coherent ONN accelerator and reduce classification accuracy across CNN models by up to 7.49% to 80.46% by just targeting 10% of MRs. We then propose techniques to enhance ONN accelerator robustness against these attacks and show how the best techniques can effectively recover the accuracy drops.

关键词： Deep learning Technological innovation Analytical models Accuracy optical resonators optical computing optical fiber networks Convolutional neural networks Trojan horses Security

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

16th International Conference on Genetic and Evolutionary Co...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

ISBN: (纸本)9789819615346

The proceedings contain 90 papers. The special focus in this conference is on Genetic and Evolutionary computing. The topics include: Individuality Prediction of Pain Facial Expressions Based on Permanent Wrinkles;In Silico Assessment of 16S rRNA Primer Specificity for Targeted Bacterial Amplification: A Case Study with Vibrio Parahaemolyticus;A Phylogenetic Update of Azospirillum Using Recent 16S rRNA Data;Vision Enhancement System with AR Wearable Aid for Low-Vision People;evacuation Support System for Tsunami Disasters that Considers Shelter Congestion;evaluation of Body Condition Score for Walking Dairy Cows Using 3D Camera;research on Individual Identification of Walking Cows Using a 3D Camera;a Graph Clustering Algorithm Based on Gravitational Vector;Handheld Pig-Weight Estimation Using an RGB-D Sensor;a Technique Based on Deep Learning for Classifying Rice Leaf Disease Using Pre-trained models;facial Expression Recognition Using Convolutional neural network with Residual Units and Attention Mechanism;Mask Segmentation and Evaluation Based on Mask Scoring R-CNN;identification of Rumination Patterns in Cattle Through optical Flow Analysis and Machine Learning Techniques;a Study on Health Management by Behavior Analysis of Calves;performance Evaluation of Edge Detectors for Automated Book Spine Segmentation in Library Bookshelf Images;performance Investigation of a New Chaotic Search Method for Vehicle Routing Problem with Drones;An Abnormal Behavior Recognition System for Bathrooms with Using Toilet Based on Thermal Image AI Edge computing Technology;detection of Script Reading Face in Diet Deliberation Video;a Smart Sensor for Gait Analysis of Rehabilitation Assessment;Effective Task Scheduling Using Particle Swarm Optimization (PSO) with Various Offloading Strategies in Fog-Cloud computing Environment.

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

16th International Conference on Genetic and Evolutionary Co...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

ISBN: (纸本)9789819615308

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neural network Watermarking with Hierarchical Recoverability

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Huang, Lin Qin, Chuan Feng, Guorui Zhang, Xinpeng University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai University School of Communication and Information Engineering Shanghai200444 China

In recent years, neural network models have been widely used in many tasks, however, tampering operations from malicious attackers, e.g., backdoor attacks and parameter malicious tampering, can easily degrade the model performance or cause a malfunction. To protect the integrity of neural network model, in this paper, we propose a neural network watermarking scheme with hierarchical recoverability (NNWHR), which not only can identify and locate the tampered parameters, but also can recover the tampered parameters in a hierarchical way. Detailedly, the parameters of to-be-protected network layers are first sorted according to the parameter importances, which are calculated through a specifically designed strategy for parameter evaluation. Then, the reference sharing mechanism is used to generate more number of recovery bits and provide greater perfect recovery probabilities for the parameters with higher importances, which can also deal with larger tampering rates through bit interleaving. Finally, the recovery bits and the authentication bits of model parameters are incorporated as watermark bits and embedded into the model redundant space after scrambling. Experimental results show that our scheme can locate tampered model parameters and recover corresponding model performance with satisfactory accuracy, and can also be applied to eliminate backdoor attacks. In addition, our scheme exhibits satisfactory generalizability, which makes it applicable to various types of neural networks. © 2004-2012 IEEE.

关键词： neural network models

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Hausa Handwriting Character Recognition Using CNN and Tesseract 2

Hausa Handwriting Character Recognition Using CNN and Tesser...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Dauda, Muhammad Khamis Musa, Umar Al-Barhamtoshy, Hassanin Faculty of Computing and Information Technology Department of Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

The Hausa language, spoken by over 64 million people, is a vital part of West Africa's cultural and social fabric. Despite its importance, handwritten Hausa script recognition has little concern due to its tonal nature, unique characters, and limited datasets. This study tackles these challenges by creating a dataset and evaluating two models commonly used in handwritten recognition: a Convolutional neural network (CNN) and Tesseract optical Character Recognition (OCR). The custom CNN, trained on data from 30 volunteers with varied writing styles, achieved an accuracy of 96%, with strong precision, recall, and F1-score metrics. In contrast, Tesseract OCR struggled, particularly with Hausa-specific characters like , , , and , achieving lowest accuracy for these classes. Although it had non-zero recognition in some characters, its performance was inconsistent, indicating difficulties in generalization. This work provides a scalable solution for handwritten Hausa character recognition, contributing to research on low-resource African languages and paving the way for better access to Hausa handwritten literature and integration into natural language processing applications. © 2025 IEEE.

关键词： optical character recognition

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Brahmi Script Recognition Using Optimized Convolutional neural network with Random Forest Classifier

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International Journal of High Speed Electronics and Systems 2025年第0期

作者： Jain, Trang Jain, V.K. Jain, A. College of Computing Sciences and It Teerthanker Mahaveer University U.P. India Koneru Lakshmaiah Educational Foundation Vaddeswaram A.P. Guntur522502 India

optical Character Recognition (OCR) is widely used to digitize printed documents, extract information from forms, automate data entry, and enable text recognition in applications ranging from license plate recognition to handwritten document conversion. Machine learning and deep learning models have recently improved OCR performance, however hyperparameter tuning remains an issue. To solve this, this paper proposes an efficient method for recognizing Brahmi script characters that combines a Convolutional neural network (CNN) with a random forest classifier. First, a CNN-based autoencoder extracts features from Brahmi script images, which are then input into the random forest classification model. The hyperparameters of both models are optimized using a genetic algorithm (GA). Extensive experimental results reveal that the proposed approach achieves a significantly better accuracy of 97%, over competitive models. © 2025 World Scientific Publishing Company.

关键词： Convolutional neural networks

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