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An improved pulse coupled neural networks model for semantic IoT

Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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UEBCS: software Development technology Based on Component Selection 18th

UEBCS: Software Development Technology Based on Component S...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Yingnan Qi, Xuezhao Li, Jian Lu, Dan College of Computer Science and Technology Harbin Engineering University Harbin150009 China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (纸本)9789819998920

Current software development has moved away from the traditional manual workshop model and emphasizes improving software product quality. To address the issue of repetitive work, software reuse techniques can be adopted to continually enhance the quality and efficiency of software development. software reuse primarily involves reutilizing existing software knowledge during the software development process, effectively reducing maintenance costs incurred during development and controlling the overall software development expenses. software components are an effective form of supporting software product reuse and serve as the core technology for enabling software reuse. Component-based software engineering techniques emphasize the use of reusable software "components" to design and construct programs, aiming to assemble these components within a software architecture to achieve software reuse and improve the quality and productivity of software products. However, selecting the most suitable components from the results of component retrieval requires understanding the different usages of each component in the retrieval results. The existing methods suffer from excessive reliance on manual approaches and errors caused by inter-component relationships. Therefore, this paper proposes a component selection technique called UEBCS (Usage Example-Based Component Selection). This technique leverages steps such as clustering analysis and hierarchical classification to achieve optimal component selection. UEBCS has shown excellent results in terms of both efficiency and accuracy in selecting components. This method provides technical support for software developers in the software development process and holds significant practical significance for enhancing software development quality and efficiency, as well as promoting the deepening development of the software industry. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer software reusability

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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Keyword extraction method based on complex network

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International Journal of Information and Communication technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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An Integrated Machine Learning Framework Based Liver Disease Diagnosis System

An Integrated Machine Learning Framework Based Liver Disease...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Imtiaz, Irsa Qaiser, Ayesha Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Viral hepatitis increases the likelihood of developing liver cancer, which is a critical public health concern. It is a severe disorder that can be fatal if not recognized and treated promptly. The most common causes of liver cancer are hepatitis viruses. Early-stage diagnosis of HCC is crucial for improving patient outcomes, particularly when hepatitis virus infection is the underlying cause. This study looks into the use of ML algorithms for early detection of liver cancer in hepatitis virus-infected patients. The study will present a unified approach for the early detection of liver cancer that is highly accurate. Early detection of liver cancer is critical in reducing mortality and assisting practitioners in treating patients on time based on results. Machine learning algorithms have been intensively researched and applied to the diagnosis of liver cancer, especially in recent years. Researchers employed a variety of machine learning techniques to create models for detecting liver cancer at different stages, including early diagnosis. To enhance the model's flexibility and efficiency, We used three datasets that contained all of the information required for diagnosing liver cancer. Several machine-learning approaches were used to improve the model's accuracy. Our proposed model acquired a 98.10% accuracy rate with Bagging. Using several datasets in machine learning for liver disease detection enhances model performance by diversifying the training examples, resulting in a more robust and generalizable model with lower overfitting risk. This study aims to improve the quality of life for people who are at risk of or have been diagnosed with liver cancer. © 2024 IEEE.

关键词： Viruses

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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A Comparison of the Performance of ResNet and DenseNet for Obstacle Avoidance and Path Following on a Mobile Robot Using Embedded GPUs

A Comparison of the Performance of ResNet and DenseNet for O...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Ma, Shengjun Karkal, Pawan Sudhir Ma, Jing Alam, Fakhrul Auckland University of Technology Electrical and Electronic Engineering Department Auckland New Zealand Auckland University of Technology Computer Science and Software Engineering Department Auckland New Zealand

ISBN: (纸本)9798331539917

In this study, we evaluated the performance of deep learning-based navigation models on an novel platform that combines the Jetson Xavier NX with an RC four-wheel-drive car for autonomous driving. Focusing on data collection and model training using a single-camera setup, obstacle avoidance and path navigation were prioritised. The study compares the effectiveness of ResNet18, ResNet34, and DenseNet121 models by analysing training time, accuracy, obstacle avoidance efficiency, and path navigation capabilities. The results highlight the computational advantages of the Jetson Xavier NX over the Jetson Nano, demonstrating enhanced real-time processing and decision-making capabilities. This integration of the Jetson Xavier NX with an RC vehicle not only improves overall performance but also introduces a novel approach to autonomous vehicle development. The findings offer valuable insights into optimising deep learning models and hardware configurations for improved autonomous navigation. © 2024 IEEE.

关键词： Mobile robots

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An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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