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Dynamic Low-Rank Adaptation Based Pruning Algorithm for Large Language Models

Dynamic Low-Rank Adaptation Based Pruning Algorithm for Larg...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Linfeng Li Lei Guo School of Computer Science and Technology Xinjiang University Urumqi China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Deep neural networks have been getting better and better performance while the parameter size has become larger and larger, and have now entered the era of large language models (LLM). However, large language models with huge number of parameters are difficult to be deployed on edge devices with limited computing power and memory resources. Network pruning techniques can reduce the storage and computation requirements of large language models. Although Dynamic Low-Rank Adaptation (DyLoRA) allows efficient fine-tuning of LLM, the deployment of LLM is still hindered by large model sizes and computational costs. In this paper, we propose a DyLoRA-based pruning algorithm that uses gradient updating of DyLoRA module to define the importance of parameters, and then prune the least important parameters based on the resulting importance scores. Through experiments on commonly used evaluation datasets, it is demonstrated that the proposed pruning algorithm is able to achieve 96.60% of the performance of the original model with 20% of the parameters pruned, which outperforms the previous pruning algorithms.

关键词： Performance evaluation Adaptation models Heuristic algorithms Large language models Computational modeling Artificial neural networks pattern recognition Computational efficiency

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

ISBN: (纸本)9789819780921

The proceedings contain 45 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Vietnamese Text Summarization Using Transformer Approach;DQN Trader: Reinforcement Learning for Automated Trading;indirect Feature Extraction for Efficient Fingerprint recognition;A Novel and Intelligent Neuroimage Analysis for Tumor Classification with Thermo-Color Segmentation Using VGG-16;a Deep Feature Ensemble Methodology for 2D Biomedical Image Classification;detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit;convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture;performance Comparison of Different Machine Learning Classifiers for Diabetes Prediction;Intelligent Anti-tamper Systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT;contrasting Various Cryptographic Algorithms for Storage and Cloud computing Services;A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits;CrimeScene2Graph: Generating Scene Graphs from Crime Scene Descriptions Using BERT NER;a Squeeze and Excitation Framework Utilizing ResNet-152 for Alzheimer’s Disease Dementia Classification;Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs);efficient Detection of Fake Twitter Profiles on Social Media Through Hybrid Deep Learning Model;religious Sentiment Analysis and Detection on Social Media;Visualizing NASDAQ Composite Index Historical Data Using Tableau;typeface Identification for English Using Convolutional Neural Networks;Adaptive Ensemble of ML Regressors and LSTM for Stock Price Prediction;PolyNMT: Neural Machine Translation Model with Seq2Seq Encoder-Decoder System;a Rectangular Slotted Sierpinski Fractal Bowtie Antenna for Multiband Wireless Communications;enhancing Cardiac Health: Machine Learning in Coronary Artery Disease Predi

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Islanding Detection in PV-Grid System by Random Forest and Spectrogram Based LBP

Islanding Detection in PV-Grid System by Random Forest and S...

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Electrical Power Engineering, Communication and computing Technology(ODICON), Odisha international conference on

作者： Ritu Singh S. P. Shukla Archana Panda Subhendu Sekhar Sahoo Subhendu Pati Basant K Panigrahi Department of Electrical Engineering Bhilai Institute of Technology Durg India Department of Electronics and Communication Engineering SOA Deemed to be University Bhubaneswar India Department of Electrical Engineering Gandhi Institute for Technology Bhubaneswar India Department of Electrical Engineering ITER SOA University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipment's. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected and the islanding must be observed within two seconds. Basically, this paper presents a novel method for detecting islanding based upon image pattern recognition using random forest (RF) classification technique is proposed. Images are utilized to derive the local binary pattern (LBP) characteristics so as to identify non-islanding as well as islanding events. Hence, the time series signal is obtained and transformed into spectrogram images at the point of common connection. These images are utilized to extract the features using histogram of oriented gradient, which is then utilized as a feature vector input for the RF classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter to derive spectrogram image. The RF classifier's effectiveness is assessed with 5-fold cross-validations. The results from classification demonstrates that detection of islanding using RF classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.25% accuracy and detection time of 182 milliseconds.

关键词： Training Accuracy Time series analysis Feature extraction Vectors Regulation pattern recognition Spectrogram Classification tree analysis Testing

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Emotion Detection Using pattern recognition from Speech 4th

Emotion Detection Using Pattern Recognition from Speech

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4th international conference on Frontiers in computing and Systems, COMSYS 2023

作者： Somolu, Harshita Dutta, Suchibrota Ghosal, Arijit Support Analyst Cognizant Technology Solutions West Bengal Kolkata India Department of Information Technology and Mathematics Royal Thimphu College Thimphu Bhutan Department of Information Technology St. Thomas’ College of Engineering and Technology West Bengal Kolkata700023 India

ISBN: (纸本)9789819726103

Detection of emotion from speech has immense importance in our present life. Emotion detection plays an important role in music therapy of human being. But detection of emotion is a problematic task. The objective of this work is to detect the underlying emotion in the audio speech along with a high accuracy. Emotion is a very important factor in communication. It has an impact in making decisions. In the virtual world, where people depend on artificial approaches, analyzing a person’s emotion plays an important role. Thus, objective is to detect the underlying emotion in a the audio speech and assign it to either joy, anger, boredom, sadness, fear, frustration, annoyance, satisfaction or neutral or any other emotion. This work considers neutral, happy, sad, and angry emotions only from speech signal. Emotions within speech mostly vary in the frequency domain. MFCC (mel-frequency cepstral coefficients) is a very good aural facet for capturing different emotions within speech. MFCC is supported by some other popular frequency domain aural facets as well as some time domain aural facets. ZCR (zero crossing rate) and STE (short time energy) have been used as time domain aural facets, and spectral roll-off, skewness, and spectral flux have been used as frequency domain aural facets. For precise observation, co-occurrence-based facets have also been considered for spectral flux as well as MFCC. For classification task, multi-layer perceptron (neural network), Random Forest, Naïve Bayes, and Logistic Regression have been used. Experimental result exhibits superiority of the work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion recognition

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Efficient Regular Expression Matching and Hardware-Accelerated Finite Automata pattern recognition in NIDS

Efficient Regular Expression Matching and Hardware-Accelerat...

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international conference on Recent Trends in Advance computing (ICRTAC)

作者： Krishna Kishore Thota R. Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu India

To protect network infrastructure from the ever-changing cyber threats, Intrusion Detection Systems (IDS) perform important duties. This study introduces a sophisticated algorithm aimed at overcoming key challenges in IDS. These challenges include efficient regular expression matching, hardware implementation, and improved pattern recognition. The algorithm employs a unique technique to reduce memory specifications by using controlled faults into the pattern matching process. It builds upon perfect hashing, ensuring precise and efficient regular expression matching, making it ideal for high-speed processing in IDS applications. Additionally, the central component of a hardware-based Network Intrusion Detection System (NIDS), the technique comprises a signature match processor that is based on a field-programmable gate array (FPGA). For effective string matching, this processor makes use of a CAM-based cellular architecture. For multiple signature matches, it generates priority-encoded addresses using a binary tree structure. To enhance IDS capabilities further, the algorithm introduces an efficient method for detecting regular expression matches within network traffic text using Field-Programmable Gate Arrays (FPGAs). It presents a hardware implementation approach based on Deterministic Finite Automata (DFA), enhancing both speed and accuracy in pattern matching. This proposed algorithm provides a comprehensive solution to optimize intrusion detection by integrating innovative techniques in regular expression matching, hardware acceleration, and pattern recognition, thereby enhancing network security against springing cyber threats.

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AI-Enhanced Triboelectric Surface Analysis for Smart Urban Accessibility pattern Detection 6

AI-Enhanced Triboelectric Surface Analysis for Smart Urban A...

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6th international conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Sungheetha, Akey Rajesh Sharma, R. Mahapatra, Sheila Agrawal, Niharika Singh, Subhav Kala Rani, K. Sasi Alliance University Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

By combining triboelectric sensors with artificial intelligence, this study offers a novel method for detecting patterns in urban accessibility. We present TriboPATH (Triboelectric pattern Analysis for Transit Heatmaps), a novel framework that integrates deep learning and triboelectric signal processing for real-time accessibility pattern recognition, building on previous work in self-powered angle sensors and urban analytics. Through the use of smart surfaces equipped with triboelectric sensors, the system records movement patterns and uses a specially designed neural network architecture to convert them into accessibility measures. Our solution outperforms current approaches by achieving 94.7% accuracy in pattern recognition over a range of view distances and angles. The system provides a long-term solution for smart city accessibility monitoring, exhibiting strong performance across 13 global metropolitan datasets. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Optical character recognition

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High-performance computing system and smart speech pattern findings for intangible cultural heritage art analysis considering visual information 3

High-performance computing system and smart speech pattern f...

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3rd international conference on Inventive Research in computing Applications, ICIRCA 2021

作者： Zhou, Mingyong Fang, Nannan School of Humanities Xi'an Shiyou Univercity Shaanxi Xian710065 China

ISBN: (纸本)9780738146270

This paper studies the visual application of highperformance computing systems and artificial language recognition in intangible cultural heritage art design. First, this article introduces the advantages, disadvantages, problems and performance evaluation of high-performance computing systems in the new era. Then, a high-performance computer-based artificial language recognition algorithm is used to classify and recognize intangible cultural heritage art. Finally, use the network database and artificial language recognition algorithm to visualize the intangible cultural heritage art design. © 2021 IEEE.

关键词： Visual languages

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Recurrent and Mixture Density Architecture-based Driver pattern recognition

Recurrent and Mixture Density Architecture-based Driver Patt...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Djamila Zamouche Kahina Ouazine Mohamed Benaissa Djalal Bouarouri Sofiane Aissani LIMED Laboratory University of Bejaia Bejaia Algeria LIMED Laboratory Estin of Bejaia Bejaia Algeria LIST3N Laboratory University of Technology of Troyes Troyes France

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

Within the framework of Intelligent Transportation Systems (ITS), recognizing driving patterns is becoming of crucial importance, given the alarming rise in accident rates resulting from imprudent conduct behind the wheel. As vehicles become more advanced, the ability to identify and deal with risky behavior have become a key factor in improving road safety. In this work, we propose an approach that distinguishes between safe and unsafe driving, enabling self-control and adaptation of the driver's actions if necessary. The main idea of our solution is the use of LSTM-based Recurrent Neural Networks (RNN) combined with the Gaussian Mixture Method (GMM), which enables classification of driving styles into three distinct categories. The performance of the proposed approach has been evaluated using important evaluation metrics. The results show that the proposal offers encouraging performance, achieving an accuracy of over 93% for different driving styles.

关键词： Wireless communication Wireless sensor networks Recurrent neural networks Accuracy Computational modeling Wheels Probabilistic logic pattern recognition Proposals Vehicles

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Role of Machine Learning in Acute Stroke Imaging: A Technical Review

Role of Machine Learning in Acute Stroke Imaging: A Technica...

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Advanced computing and Emerging Technologies (ACET), international conference on

作者： Gurmeet Singh Mahender Singh Akansha Gupta Deepjyot Singh Department of Electronics and Communication Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India Department of Computer Science and Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic Stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic Stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding.

关键词： Neuroimaging Machine learning algorithms Accuracy Prevention and mitigation Imaging Machine learning Stroke (medical condition) Prediction algorithms Planning pattern recognition

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Modified Artificial Hummingbird Algorithm with Deep Learning for Pest Detection through Leveraging pattern recognition-based Fusion Approach

Modified Artificial Hummingbird Algorithm with Deep Learning...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： B. Rubadevi Helina Rajini Suresh Ibrahim Abdullah Prashant Kumar Choudhary Minu Choudhary Piyush Mathurkar Department of Computer Science and Engineering Arunai Engineering College Tiruvannamalai Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science Department Al-Turath University Baghdad Iraq Department of Electronics and Communication Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The limitations of manual inspection and simple algorithms highlight the need for advanced insect detection systems in agriculture. The study presents the Modified Artificial Hummingbird Algorithm with Deep Learning (MAHADL), a significant breakthrough in pest detection. MAHADL combines the speed and efficacy of the Artificial Hummingbird Algorithm (AHA) with deep learning models such as Recurrent and Convolutional Neural Networks. Combining pattern recognition with the fusion approach improves pest detection accuracy while reducing false positives and negatives. Accurate pest identification is essential for effective agricultural management, which MAHADL provides through data collection, preprocessing, and algorithmic integration. MAHADL outperforms existing systems in terms of accuracy (0.94), recall (0.95), precision (0.92), computational economy (12 hours of training time, 0.5 seconds/image inference time), and decrease in false positives and negatives. The innovative technique provides enhanced agricultural output and sustainability, representing a significant leap in intelligent pest management.

关键词： Deep learning Training Accuracy Insects Manuals Inference algorithms pattern recognition

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