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Cognitive Computing with Deep Neural Networks 4

Cognitive Computing with Deep Neural Networks

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sikarwar, Shailendra Singh Rathore, Rachna Upadhyay, Shivam Lakra, Ankita Soumya, M. Miglani, Sumit Jiwaji University Pgv College Dept. of Computer Science Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Eee Aligarh India Usha Martin University Dept. of Cse Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

this study introduces a novel approach to enhancing question answering (QA) systems, employing a RoBERTa based architecture and complexity-enhanced input features. the research is structured into four main components: training methodology, feature engineering, model construction, and data preprocessing. A Python function is proposed to calculate linguistic difficulty metrics for input sentences using readability measures and natural language processing techniques. Tensor-Flow Datasets (TFDS) are then utilized to load and preprocess the SquAD (Stanford Question Answering Dataset) dataset, facilitating efficient training. To provide contextual information to the input representation, word embeddings from pre-trained GloVe vectors are integrated with complexity metrics to prepare input features. Incorporating these enriched features, a distinctive question-answering model based on the RoBERTa architecture is trained using the AdamW optimizer and Cross Entropy Loss. the training process employs iterative epochs to optimize the model's parameters and minimize the loss function. An independent validation dataset is employed to assess the model's performance, demonstrating the effectiveness of the proposed method in enhancing the accuracy and robustness of the QA system. In conclusion, this study presents a systematic approach to enhancing system quality by combining state-of-the-art neural architecture with input properties augmented by complexity. © 2024 IEEE.

关键词： Deep neural networks

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Research on weak signal detection based on wavelet-correlation detection algorithm 4

Research on weak signal detection based on wavelet-correlati...

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4th international conference on Digital Signal and Computer Communications, DSCC 2024

作者： Cheng, Biyao Li, Ming Wang, Xiaozhuo Science and Technology on Electromechanical Dynamic Control Laboratory Xi’an710065 China Xi’an Institute of Electromechanical Information Technology Xi’an710065 China School of Astronautics Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9781510681538

the detection of weak signals has a wide range of applications in engineering. In this paper, a combined algorithm of wavelet transform and correlation detection is proposed to address the problems in current detection methods, namely the wavelet-correlation detection algorithm, to detect weak signals. By building a Simulink model on Matlab and implementing simulation through programming, the reliability of the algorithm is verified. the detection performance of the algorithm for weak signals in fixed noise and color noise environments is analyzed, and compared with the cross-correlation detection method. the results show that the wavelet-correlation detection algorithm can distinguish weak signal features from strong noise backgrounds. Taking the 5.2943dB input signal as an example, the wavelet- correlation detection algorithm improves the relative error of amplitude from 1.89% to 0.35%. Signal-to-noise ratio increased by 8.7638dB. © 2024 SPIE.

关键词： Wavelet transforms

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BumbleBee: the Multi-purpose Voice Assistant 1

引用

4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Chakraborty, Devottam Pandey, Devansh Verma, Rajat Dhanda, Namrata Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Voice Assistants software agents to comprehend spoken language and respond with synthesized voices, voice assistants have revolutionized human-computer interaction. An application programming interface (API) called Bumblebee enables frictionless communication between users and Windows operating systems (OS). By eliminating the need for manual input using conventional input devices like keyboards or touchscreens, this voice control integration with Windows OS enhances user productivity and convenience. Users may easily control their environment, get information quickly, and streamline their daily activities by taking into account Bumblebee’s capability. In the end, Bumblebee is a strong API that enables users to interact with their Windows PCs via voice commands. By utilizing the power of natural language, it makes simpler to complete tasks like managing emails, controlling devices, organizing tasks, and executing requests. Voice control’s seamless integration with Windows OS completely changes how people interact with their computers, making it more simple, efficient, and user-friendly. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Application programming interfaces (API)

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A Deep Learning Algorithm Improvement for Brain Tumour Segmentation Using Kernel-Based CNN and M-SVM 4

A Deep Learning Algorithm Improvement for Brain Tumour Segme...

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Nishant Kumar Verma, Pushpneel Bhagwant University Department of Computer Science & Engineering Rajasthan Ajmer India

ISBN: (纸本)9798350364729

Recently, medicinal imaging has shown significant growth in demand due to its ability to provide useful information from human anatomical images, which helps understand symptoms and diagnose patients (Garzón & Cardenas-Villa 2020), with astrocytoma's separation among the crucial tasks popular the field that can assist clinicians on diagnosis, treatment planning as well as monitoring of patient they are concerned about. Deep learning algorithms have started to show impressive results in the segmentation of brain tumors from MRI images over recent years. therefore, this study presents a new deep learning algorithm that utilizes kernel-based Convolutional Neural Networks (CNN) and multi-class Support Vector Machines (M-SVM) for more correct separation of astrocytoma's. the latter is a kernel-based CNN method that efficiently learns deep structures as of the contribution MRI pictures. the features remain input into the mm classifier, which accurately classifies them to different tumor regions (necrotic core, edema and enhancing tumor). Besides, we introduce a boundary refinement module into the framework to fine-tune results at segmentation boundaries for better accuracy. Here, we attempted to analyze our analyze algorithm over publicly available annotation datasets experimentally and compared them against the state-of-the-art as baselines. Our approach outperformed previous studies with an normal Dice comparation constant of 0.93, illustrating the capacity to segment brain tumor regions accurately. In addition, our algorithm demonstrated a resilience to noise and differences in image resolution. © 2024 IEEE.

关键词： Convolutional neural networks

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Infrared Small Target Detection Algorithm Based on Super-Resolution Reconstruction and Improved YOLOv8n 4

Infrared Small Target Detection Algorithm Based on Super-Res...

引用

4th international conference on Control theory and Applications, ICoCTA 2024

作者： Shen, Yukuang Yu, Jian Fan, Huili Zhang, Xingchen Wu, Xiang Nanjing University of Science and Technology School of Automation Nanjing China China Ship Development and Design Center Wuhan China

ISBN: (纸本)9798331506155

Infrared small target detection primarily involves the recognition and localization of small targets within infrared images, holding great potential for various applications. this task faces several technical challenges due to the small size of targets in infrared images and the complexity of their backgrounds, including the low signal-To-noise ratio and limited pixels of the targets. We propose an improved YOLOv8n detection model based on sample super-resolution reconstruction to address these issues. First, the Spatially-Adaptive Feature Modulation (SAFMN) super-resolution method is employed to improve the resolution of the sample images and enhance the texture features of the targets. Subsequently, the processed images are cropped into smaller patches, and only patches containing targets are retained to strengthen the features of the infrared small targets and reduce the interference of the background. Finally, we integrate a parallelized patch-Aware attention (PPA) module into the YOLOv8n network, which uses a multi-branch feature extraction strategy to capture features at different scales and levels of the targets. Experimental results show that, compared to the state-of-the-art detection methods, our method improves the mAP50 by 2.4% and Precision by 6.3% on the IRSTD1k dataset. © 2024 IEEE.

关键词： Infrared imaging

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A Feature Selection Method based on Sparse Regularization Deep Neural Network 4

A Feature Selection Method based on Sparse Regularization De...

引用

4th international conference on Computer engineering and Application, ICCEA 2023

作者： Sun, Junjun Chen, Sibao School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9798350347548

Data dimensionality reduction is a technology that converts high-dimensional data into low-dimensional data. With the continuous growth of data dimensions, data dimensionality reduction is essential in both industrial applications and scientific research. Feature selection is an effective method to achieve dimensionality reduction. However, most existing feature selection methods can only be applied to small datasets, and they cannot be used in big datasets or have poor performance. In order to overcome this dilemma, A novel feature selection method based on sparse regularization deep neural network, called Sparse Regularization Deep Feature Selection (SRDFS), is proposed. SRDFS utilizes deep neural network and sparse regularization to realize feature selection, which has a good performance on big datasets. In addition, this paper proposes an optimization algorithm based on soft threshold function for sparse regularization. Finally, we compare the proposed method with the related state-of-the-art feature selection methods on six big datasets, and the experimental results show the effectiveness and superiority of the method. © 2023 IEEE.

关键词： Feature Selection

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Development of a Smart Edge Device for Home Security 4

Development of a Smart Edge Device for Home Security

引用

4th international Informatics and Software engineering conference, IISEC 2023

作者： Lee, Chen Shyan Kit Mun, Hou Universiti Tunku Abdul Rahman Faculty of Engineering and Science Department of Mechatronic and Biomedical Engineering Bandar Sungai Long Selangor Malaysia

ISBN: (纸本)9798350318036

Home security remains an essential consideration in this decade, as property crime continues to be a significant concern in Malaysia. Amidst the COVID-19 pandemic, where preventing virus transmission is of utmost importance, integrating home security with the Internet of things (IoT) technology has become increasingly relevant. this study aims to develop a smart edge device for enhancing the security of houses in residential areas. A real-Time facial recognition security system is constructed using You Only Look Once (YOLO) version 7, in conjunction with the Facenet models. these models have been trained, evaluated, and integrated with a Raspberry Pi to establish communication with the user through Node-RED and the Telegram User Interface (UI), utilizing the Message Queuing Telemetry Transport (MQTT) protocol. the performance of the developed facial recognition security system is compared with other state-of-the-art systems in terms of facial recognition accuracy and inference speed on edge devices. the proposed algorithm achieves a remarkable 94.14% accuracy at a speed of 0.8 FPS. © 2023 IEEE.

关键词： Face recognition

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Improving Citrus Farming through Efficient and Accurate Diagnosis of Lemon Citrus Canker Disease with Deep Learning 4

Improving Citrus Farming Through Efficient and Accurate Diag...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Kukreja, Vinay Sharma, Rishabh Yadav, Rishika Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335750

Lemon citrus canker disease is a devastating bacterial disease that affects citrus trees and causes significant economic losses for citrus growers worldwide. the early detection and accurate classification of citrus canker disease are crucial for disease management and control. Within the scope of this research article, we suggest a hybrid model that combines a CNN and SVM for the multi-classification of lemon citrus canker diseases. the proposed model is trained on a dataset of six severity levels of citrus canker, ranging from healthy to severely infected, collected from citrus farms in Punjab, India. On the testing set, the suggested model achieved an overall accuracy of 94.03%, which was higher than the performance of other state-of-the-art models. the model that was presented has the potential to increase the accuracy and efficiency of citrus canker detection and classification, which can assist citrus producers and inspectors reduce the spread of the disease and minimize economic losses. © 2023 IEEE.

关键词： Image enhancement

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Weight Distribution Based Model Compression Method for Object Detection System 4

Weight Distribution Based Model Compression Method for Objec...

引用

4th international conference on Big Data, artificial Intelligence and Internet of things engineering, ICBAIE 2023

作者： Zhang, Junrong Zhang, Minghe Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China

ISBN: (纸本)9798350343618

artificial intelligence-based object detection is an essential field in computer vision applications such as autonomous driving, security surveillance, medical image analysis, intelligent transportation, and human-computer interaction. However, such state-of-the-art algorithm is computing and resources intensive, which limits its deployment especially on resources constrained devices such mobile phones, drones, and autonomous vehicles. In this paper, we propose a compression method to overcome such bottleneck. Our approach reduces the computing complexity by automatically zeroing out certain percentage of weights in convolutional layers. through extensive experiments on the well-known YOLOv5s model, 60% computing can be saved during the inference process without sacrificing the (mean Average Precision) mAP. © 2023 IEEE.

关键词： Autonomous vehicles

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LLM4PLC: Harnessing Large Language Models for Verifiable programming of PLCs in Industrial Control Systems 44

LLM4PLC: Harnessing Large Language Models for Verifiable Pro...

引用

ACM/IEEE 46th international conference on Software engineering - Software engineering in Practice (ICSE-SEIP)

作者： Fakih, Mohamad Dharmaji, Rahul Moghaddas, Yasamin Araya, Gustavo Quiros Ogundare, Oluwatosin Al Faruque, Mohammad Abdullah Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Siemens Technol Princeton NJ USA

ISBN: (纸本)9798400702174

Although Large Language Models (LLMs) have established predominance in automated code generation, they are not devoid of shortcomings. the pertinent issues primarily relate to the absence of execution guarantees for generated code, a lack of explainability, and suboptimal support for essential but niche programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to produce valid programs for Industrial Control Systems (ICS) operated by Programmable Logic Controllers (PLCs). We propose LLM4PLC, a user-guided iterative pipeline leveraging user feedback and external verification tools - including grammar checkers, compilers and SMV verifiers - to guide the LLM's generation. We further enhance the generation potential of LLM by employing Prompt engineering and model fine-tuning through the creation and usage of LoRAs. We validate this system using a FischerTechnik Manufacturing TestBed (MFTB), illustrating how LLMs can evolve from generating structurally-flawed code to producing verifiably correct programs for industrial applications. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. the proposed pipeline improved the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 2.25/10 to 7.75/10. To promote open research, we share the complete experimental setup, the LLM Fine-TuningWeights, and the video demonstrations of the different programs on our dedicated webpage(1).

关键词： Industrial Control Verifiable Synthesis Large Language Models Prompt engineering

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