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Journal of Network Intelligence 2024年第3期9卷 1803-1819页

作者： Li, Honghui Zhao, Tianming Fu, Jiangyin Fu, Xueliang Department of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China School of Electronics and Informaiton Engineering Harbin Institute of Technology Harbin150006 China

Lakes in cold and arid regions play an important role in the daily lives of people. However, the health of the lake ecosystem is seriously affected by climate change, especially human activities. Remote sensing satellites provide effective means for lake ecological health monitoring. How to extract lakes in cold and arid regions from remote sensing images has become a research hotspot. There exist some issues in most of the traditional methods regarding extraction accuracy as well as susceptibility to interfer-ence. Therefore, this paper proposes a novel method named AU-net, which is based on the improved U-net model and Fully Connected Conditional Random Field (FCCRF). Firstly, semantic segmentation datasets are established using Landsat 8 OLI remote-sensing images for the intended research objects, i.e., Wulliangsuhai Lake and Hulunhu Lake. Then, the U-net model is constructed. To improve the model’s adaptive ability, the Convolutional Block Attention Module is added after the feature map is extracted from the model. Finally, post-processing is conducted based on the FCCRF. The experimental results depict that the AU-net extraction accuracy is superior to the Normalized Difference Water Index, Deeplab v3+ model, and the initial U-net model. Moreover, AU-net can eliminate the interference of complex backgrounds and small holes. Further-more, AU-net has achieved good extraction results for Wuliangsuhai Lake and Hulunhu Lake in different seasons. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Semantic Segmentation

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Regional Forest Fire Detection and Control: An Integrated Approach Using Distributed Sensing and Automated Suppression Systems 5

Regional Forest Fire Detection and Control: An Integrated Ap...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Marathe, Abha Kadam, Manas Mohite, Shreya Narayanrao Lasurkar, Prachi Lanjewar, Riya Modhave, Pranav Vishwakarma Institute of Technology Electronics and Communication Engineering Pune India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331518097

Forest fires present a significant hazard to property, lives, and ecosystems globally, necessitating swift detection and containment measures. Historically, manual forest fire control systems relied on human observation and reporting, which often resulted in delayed responses and increased damage. The forest is divided into target areas, with each area equipped with intelligent software interfaces, automated response mechanisms, flame sensors, and sprinklers. The proposed system relies on advanced flame sensor technology for prompt and accurate fire outbreak detection. Upon detection, real-time fire data is transmitted to a cloud-based platform, ***, for comprehensive monitoring and analysis. This division allows for targeted intervention and efficient resource allocation based on the severity and location of the fire. The flame sensors used in this system have a range of up to 100 meters, allowing for extensive coverage of forest areas. Additionally, the system includes automated sprinklers that activate immediately upon fire detection, providing an immediate response to help contain and extinguish the fire. By leveraging *** for data transmission and analysis, stakeholders gain valuable insights into fire behavior, facilitating informed decision-making and long-term mitigation strategies. This integrated approach offers a comprehensive solution to combat forest fires, emphasizing early detection and efficient containment to minimize damage and protect both natural habitats and human communities. © 2024 IEEE.

关键词： Sprinkler systems (irrigation)

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Business Response Prediction based on Consumer Behaviour Patterns 9

Business Response Prediction based on Consumer Behaviour Pat...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Pokharkar, Vishwa Edgaonkar, Omkar Shinde, Aryajeet Nirmal, Nilesh Pune Institute of Computer Technology Electronics & Telecommunications Engineering Department Pune India

ISBN: (纸本)9798350394474

This survey paper presents a comprehensive analysis of predictive models for business response in the hospitality industry, focusing on cafes and restaurants. Leveraging data from Zomato, a popular restaurant aggregator platform, and supplemented with location data, we explore effective strategies for preprocessing and clustering based on key parameters, including location, cuisine, nearby institutions, parking, traffic, bestsellers, cost, ratings, delivery options, and establishment type (cloud kitchen/dine out). We investigate a variety of machine learning and statistical approaches tailored to distinct clusters of cafes and restaurants. Our evaluation involves training and testing models to predict business responses, considering varying customer foot traffic and preferences. The findings emphasize the importance of precise clustering and customized predictive models for improved accuracy in response forecasting. The insights derived from this survey are anticipated to significantly impact decision-making processes for businesses in the hospitality sector. © 2024 IEEE.

关键词： Forecasting

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Single-Frequency Self-Alignment RF Resonant Beam for Information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2014 IEEE.

关键词： Inductive power transmission

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Resource allocation for wireless networks based on enhanced Harris hawks optimisation algorithm

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International Journal of Security and Networks 2024年第4期19卷 178-187页

作者： Meng, Xianmeng Cai, Cuicui Tan, Linglong Wang, Qijian School of Electronics Engineering Anhui Xinhua University Hefei230088 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Electronics and Information Engineering West Anhui University Lu’an237012 China

Efficient resource allocation is critical to improve the quality of service in wireless networks. The problem of resource allocation is usually non-convex and non-deterministic polynomial-hard. Meta-heuristic algorithms are widely used to solve non-convex optimisation problems due to their efficient execution and high iteration accuracy. The Harris hawk optimisation (HHO) algorithm has recently received attention as an effective approach for addressing various optimisation problems. However, the HHO algorithm is employed for resource allocation issues in WNs and suffers from early convergence and local optimal problems. To overcome these issues, we develop a novel enhanced HHO (EHHO) algorithm with low computational complexity and explore the performance of the EHHO in resource allocation. Firstly, we introduce the power allocation for secrecy rate maximisation and spectrum efficiency trade-off in wireless networks. Then, the fundamental of the basic HHO algorithm is described, and the EHHO algorithm is proposed with nonlinear control update and local search strategies. Finally, we demonstrate the remarkable capabilities of the EHHO in terms of the benchmark functions and resource allocation in wireless networks. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： energy efficiency enhanced Harris hawks optimisation algorithm meta-heuristic optimisation resource allocation secrecy rate spectral efficiency

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Performance Analysis of Denoising Filters for Ultrasound Fetal Head Images

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SN computer Science 2025年第2期6卷 1-13页

作者： Prasad, Kauleshwar Patnaik, Pawan Kumar Agrawal, Anupam Department of Computer Science and Engineering Bhilai Institute of Technology Chhattisgarh Durg India Department of Electrical and Electronics Engineering Bhilai Institute of Technology Chhattisgarh Durg India

Deep learning has significantly advanced image processing in the medical domain, including the analysis of ultrasound (US) fetal images for prediction of fetal growth retardation. Before analysis of fetal images there is need of correction since they are usually adulterated by noise during acquisition of image. In the recent years, there are various deep learning based denoising methods in which U-Net have attracted the attention of many researchers. This paper describes the comparative study of different filters such as mean filter, gaussian filter, anisotropic diffusion, non-local means (NLM) filter and U-Net based on their performance on denoising fetal images prior to the prediction of fetal growth retardation (FGR). In this article peak signal to noise ratio (PSNR), mean square error (MSE), similarity index measure (SSIM), peak signal to interference ratio (PSIM) and multi scale structural similarity index measure (MSIM) values are analyzed for the performance of these filters. This paper reveals that U-Net performs better in image quality evaluation when compared to other filtering algorithms, based on testing and analytical findings. We have also discussed the significance of MSE, PSNR, SSIM, PSIM, MSIM and U-Net in this article. This article also covers the advantages, disadvantages, and characteristics of different filtering methods, including U-Net. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Denoising techniques Fetal growth retardation U-Net

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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

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computers, Materials & Continua 2024年第11期81卷 3125-3145页

作者： Sonia Goel Meena Tushir Jyoti Arora Tripti Sharma Deepali Gupta Ali Nauman Ghulam Muhammad Electrical and Electronics Engineering(EEE)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Information Technology(IT)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of Computer Science and Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11421Saudi Arabia

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

关键词： Incomplete data nearest neighbor linear interpolation imputation clustering classification

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A Machine Learning and Deep Learning-Based Web Application for Crop and Fertilizer Recommendation and Crop Disease Prediction 6th

A Machine Learning and Deep Learning-Based Web Application f...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Srinidhi, Amuri Jahnavi, Veeramachinani Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819984503

India is a country that is majorly dependent on agriculture and farming for livelihood. The country’s diverse soil lets farmers produce various crops throughout the year. Depending on the characteristics of soil such as the composition of nitrogen, phosphorus, potassium and the pH of the soil and environmental factors such as rain, humidity, and temperature, it is important to know which crops should be grown and what fertilizers to use, to maximize the yield. It is also important to detect any crop diseases at an early stage to prevent major loss. This paper presents an all-in-one platform web application that performs all three tasks, and it uses ML algorithms such as KNN, Decision Tree, Gaussian Naive Bayes, SVM, Logistic Regression, and Random Forest to recommend crops and fertilizers by analyzing soil and environmental factors. For crop recommendation, Random Forest is chosen as it provided the highest accuracy of 99.09%. Random Forest is deployed for fertilizer recommendation as well, as it provided the highest accuracy of 100%. The Deep Learning model ResNet50 is used to detect crop diseases, and the accuracy for training and validation was 95.52% and 87.36%, respectively. Flask framework is used to build the backend of the web application. The web application is enabled to send an SMS of the results to user’s mobile phone using the SMS API, Twilio. The goal of the project is to provide an ideal solution to farmers or the users of the web application, via a text message, which can be accessed even in remote areas and can be understood by the majority of people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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A Comparative Study of Pure and Silver-Enhanced Graphene Antennas for Fat-Intrabody Communication 10

A Comparative Study of Pure and Silver-Enhanced Graphene Ant...

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10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Subramaniam, Sree Aran Asan, Noor Badariah Aziz, Mohamad Zoinol Abidin Abd. Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology & Engineering Engineering Department Durian Tunggal Melaka Malaysia Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology & Engineering Technology Department Durian Tunggal Melaka Malaysia

ISBN: (纸本)9798350364163

This paper presents the design and comparative study of two implant antennas utilizing graphene material for fat-intrabody communication (fat-IBC) in the 5.8 GHz Industrial, Scientific, and Medical (ISM) band. Both antennas use pure graphene and silver-enhanced graphene as radiating elements, fabricated on a Taconic TLY-3 substrate. Copper-conducting elements were used as the substitute conductive material in CST Microwave Studio simulation for the optimized designs. These antennas were then measured in a fat-tissue-equivalent phantom model, and the S11reflection coefficients were recorded. These results show that the silver-enhanced graphene antenna has better performance in conductivity and resonance sharpness compared with the pure graphene antenna, which can be applied for the fat-IBC system. © 2024 IEEE.

关键词： Microwave antennas

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