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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

SN computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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PulmoWave: Pulmonary Disease Detection Using mmWave Sensing 17

PulmoWave: Pulmonary Disease Detection Using mmWave Sensing

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Ghosh, Sagnik Chakraborty, Sandip IIT Kharagpur Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Pulmonary diseases such as Chronic Obstructive Pulmonary Disorder (COPD), Asthma, and Pneumonia present significant global health challenges, necessitating early and accurate diagnosis for improved outcomes. Traditional diagnostic methods, including spirometry and chest X-rays, are often costly, inaccessible in remote areas, and unsuitable for real-time monitoring. This paper proposes a novel, non-invasive sensing approach using mmWave radar (IWR1843) for pulmonary disease identification. The radar system captures subtle thoracic movements, enabling the analysis of respiratory patterns and vital signs such as respiratory rate, phase variations, and vibration-based chest motion. The proposed system was evaluated across COPD and normal subjects, demonstrating its capability to differentiate between these conditions based on respiratory parameters. This solution offers significant advantages, including cost-effectiveness, portability, and accessibility, making it suitable for clinical and remote settings. With the ability to provide real-time, detailed respiratory insights, this approach holds great promise for widespread deployment in pulmonary disease diagnosis and monitoring. © 2025 IEEE.

关键词： Lung cancer

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A Comparative Analysis of Android Malware Detection Using Deep Learning 1st

A Comparative Analysis of Android Malware Detection Using De...

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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Sahu, Diptimayee Tripathy, Satya Narayan Kapat, Sisira Kumar Department of Computer Science Berhampur University Berhampur India

ISBN: (纸本)9783031487736

The fear of Android malware infection is building up with its user acceptance in the global market space. The existing anti-malware techniques are archaic to signature-based detection which limits the detection scope against newly crafted malware. Several machine learning, as well as deep learning approaches, has been proposed so far to combat against the rising threat of the android mobile world. Most of these have relied on static features due to its lower cost. In this paper, a Deep Neural Network model is proposed and evaluated it with static, dynamic and hybrid features respectively to prepare the comparative statement. In this experiment we used the OmniDroid dataset [1] which consists of 22,000 Android Package Kits (APKs) including both benign and malware APKs. For analysis 7955 static features and 4805 dynamic features are extracted using proper selection criteria. The efficacy of the proposed models on different data types is compared in terms of accuracy and loss. This experiment achieved a higher accuracy of 99.66% with dynamic features and a lower accuracy of 85.74% with combined features. Similarly, we achieved a minimal loss of 0.06 with dynamic analysis and a higher loss of 0.61 with static analysis. This confirms that the efficacy of dynamic analysis is more prominent in An- droid malware detection. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Performance Analysis of Various Feature Extraction Methods for Classification of Pox Virus Images

Performance Analysis of Various Feature Extraction Methods f...

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Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Haripriya, K.P. Hannah Inbarani, H. Department of Computer Science Periyar University Salem India

ISBN: (纸本)9789819955206

After the COVID-19 pandemic, people began to fear that the monkeypox virus will be the next epidemic. The World Health Organization (WHO) has reported that monkeypox outbreaks have taken place in different regions of Central and West Africa throughout the years, with the latest outbreak being reported in Nigeria in May 2021. Fever, swollen lymph nodes, dry cough, and red rashes all manifest as signs of the monkeypox virus. Most of the symptoms of Measles and chickenpox are comparable. These disorders are given only methodical treatment by the doctor. In Image processing, feature extraction techniques are used to transform raw pixel values of an image into a set of features that can be used for further analysis, such as object recognition, image classification, and image retrieval. Several feature extraction techniques are employed to determine the disease from the images in order to determine which technique works best for this collection of monkeypox skin images (MSID). Wavelets fused with gray-level co-occurrence matrix (GLCM), Haralick features, and local binary pattern are the various feature extraction techniques (LBP) applied in this work. For the classification of various pox virus diseases such as measles, chicken pox, and monkeypox, various classification algorithms such as Random Forest Classification (RF), Naive Bayes (NB), K-Nearest Neighbor algorithms (KNN), Support Vector Machine (SVM), Ada Boosting (AB), and Gradient Boosting (GB) are used in this work. In this paper, four evaluation metrics are used to determine the best feature extraction method for the monkeypox, chickenpox, and measles datasets. Wavelets fused with GLCM produce the highest accuracy (84.41% for gradient boosting and 83.87% for random forest) when extracting features from Monkeypox Skin Image Datasets (MSID). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Support vector machines

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MASIVE: Open-Ended Affective State Identification in English and Spanish

MASIVE: Open-Ended Affective State Identification in English...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Deas, Nicholas Turcan, Elsbeth Mejía, Iván Pérez McKeown, Kathleen Columbia University Department of Computer Science United States

ISBN: (纸本)9798891761643

In the field of emotion analysis, much NLP research focuses on identifying a limited number of discrete emotion categories, often applied across *** basic sets, however, are rarely designed with textual data in mind, and culture, language, and dialect can influence how particular emotions are *** this work, we broaden our scope to a practically unbounded set of affective states, which includes any terms that humans use to describe their experiences of *** collect and publish MASIVE, a dataset of Reddit posts in English and Spanish containing over 1,000 unique affective states *** then define the new problem of affective state identification for language generation models framed as a masked span prediction *** this task, we find that smaller fine-tuned multilingual models outperform much larger LLMs, even on region-specific Spanish affective ***, we show that pre-training on MASIVE improves model performance on existing emotion ***, through machine translation experiments, we find that native speaker-written data is vital to good performance on this task. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Description Logics with Abstraction and Refinement: From ALC to EL 21

Description Logics with Abstraction and Refinement: From ALC...

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21st International Conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Lutz, Carsten Schulze, Lukas Department of Computer Science Leipzig University Germany Germany

ISBN: (纸本)9781956792058

We study extensions of description logics from the widely used EL family with operators that make it possible to speak about different levels of abstraction. We analyze the computational complexity of reasoning and show that often, this complexity is significantly lower than in the corresponding extension of the more expressive description logic ALC. By slightly varying the semantics, we also obtain a case that admits reasoning in polynomial time. © 2024 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Knowledge representation

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A Comprehensive Survey on Clinical Models for AI-Powered Medical Applications 4

A Comprehensive Survey on Clinical Models for AI-Powered Med...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Javagal, Bhavya N Sharma, Sonal Department of Computer Science & Engineering Karnataka Bengaluru562112 India

ISBN: (纸本)9798331523923

Automatic Clinical Text Identification and Classification is a Natural Language Processing (NLP) technique for decoding clinical texts to uncover hidden information. Clinical text analysis has a wide range of applications, including document classification based on clinical significance, disease diagnosis using Electronic Health Records (EHRs), medical chatbots, and more. The widespread application of clinical text analysis has pushed researchers to automate medical text analysis. NLP can capture, process, and organize information from unstructured text data with minimal manual effort, allowing employees to focus on more productive tasks. While NLP is effective for handling unstructured text, the vast amount of medical data requires more advanced methods. To process such large volumes of data, a Large Language Model (LLM), a type of NLP, achieves more effective results. This review aims to provide comprehensive guidance on clinical text classification, starting with unstructured text collection and progressing to the development and evaluation of LLMs. Over 40 research articles are reviewed. Although several LLM models are available, the review focuses on Clinical BERT, Clinical BigBird, and Clinical Longformer. These three models are evaluated based on various characteristics, including model size, architecture, performance, error-handling capability and more. The review helps in determining the most appropriate LLM based on clinical requirements. This review article will be a valuable resource for researchers working on clinical text classification. © 2025 IEEE.

关键词： Electronic health record

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Segmenting Image and Removing Obtrusive Elements

Segmenting Image and Removing Obtrusive Elements

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Singh, Anirudh Gupta, Vishank Gupta, Yash Kamboj, Dikshant Singh, Ashwini Kumar Malik, Preeti University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

This Model offers a novel method for processing images that highlights the main subject and removes unnecessary features using YOLO, Darknet, picture segmentation, and Mask R-CNN."The approach accomplishes accurate object detection by using procedures like segmentation using Mask R-CNN, YOLO and Darknet object identification, and backdrop blackening. It is highly skilled at identifying primary items and has been trained on a large photo dataset. Its uses include autonomous driving, object detection, and image retrieval. By drastically lowering background noise, the detection component improves the accuracy of photo object detection. Essentially, this technique is critical to improving object detection accuracy through the reduction of background noise, which is an essential component of image processing. Finding and keeping important components while eliminating unnecessary ones from test cases or camera feeds is the main goal of the Model. © 2024 IEEE.

关键词： Image segmentation

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Variational Language Concepts for Interpreting Foundation Language Models

Variational Language Concepts for Interpreting Foundation La...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wang, Hengyi Tan, Shiwei Hong, Zhqing Zhang, Desheng Wang, Hao Department of Computer Science Rutgers University United States

ISBN: (纸本)9798891761681

Foundation Language Models (FLMs) such as BERT and its variants have achieved remarkable success in natural language *** date, the interpretability of FLMs has primarily relied on the attention weights in their self-attention ***, these attention weights only provide word-level interpretations, failing to capture higher-level structures, and are therefore lacking in readability and *** address this challenge, we first provide a formal definition of conceptual interpretation and then propose a variational Bayesian framework, dubbed VAriational Language Concept (VALC), to go beyond word-level interpretations and provide concept-level *** theoretical analysis shows that our VALC finds the optimal language concepts to interpret FLM *** results on several real-world datasets show that our method can successfully provide conceptual interpretation for FLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Fine-Tuning Pre-trained Model with Prototype Learning for OOD Detection 4th

Fine-Tuning Pre-trained Model with Prototype Learning for ...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Liu, Jia-Yi Deng, Zhi-Dong Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819787012

Out-of-distribution (OOD) detection primarily learns the in-distribution (ID) representation to distinguish OOD samples from ID classes. Recent methods often fine-tune a pre-trained model with classification objective (cross-entropy, CE) to learn ID representation, disregarding the discrimination between ID and unknown OOD. In this work, we propose to fine-tune pre-trained models in the framework of open-set recognition, which trains a multi-class classifier with ID samples while considering OOD rejection according to distances to ID classes. We adopt prototype learning, which aims to discriminate the ID classes while guiding compact distributions for ID classes in the feature space. The compact ID distributions favor classification and OOD detection using distances, including Euclidean distance and Mahalanobis distance. We evaluated the method on various backbone architectures including ResNet, ViTs and BEiT, and the results show that the prototype learning method on pre-trained models outperforms classification-based method and produces state-of-the-art performance in OOD detection. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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