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A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

computers, Materials & Continua 2024年第12期81卷 3561-3582页

作者： Jianyong Wang Mingliang Gao Qilei Li Hyunbum Kim Gwanggil Jeon School of Electrical and Electronic Engineering Shandong University of TechnologyZibo255000China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK Department of Embedded Systems Engineering Incheon National UniversityIncheon22012Republic of Korea

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public *** counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and *** approaches vary in terms of supervision techniques,network architectures,and model ***,most crowd counting methods rely on fully supervised learning,which has proven to be ***,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often *** a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical *** paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision *** summarize and critically analyze the key methods in these two categories,highlighting their strengths and ***,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark *** believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

关键词： Crowd counting density estimation convolutional neural network(CNN) un/semi-supervised learning

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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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science China(Information sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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Images denoising for COVID-19 chest X-ray based on multi-scale parallel convolutional neural network

Images denoising for COVID-19 chest X-ray based on multi-sca...

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作者： Ahmed, Noor Rozina Ali, Ahmad Raziq, Abdul Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Systems Engineering and Science Balochistan University of Engineering and Technology Khuzdar Khuzdar Pakistan

Chest X-ray (CXR) is a prominent and cost-effective medical imaging tool used in healthcare sectors. Coronavirus (COVID-19) has recently proliferated around the world, and different waves are returning, posing a serious threat to the global economy and health. Deep learning networks are being used to detect infected patients from CXR images, which is a huge step in making a prompt COVID-19 pre-diagnosis and reducing the workload of medical staff. However, in CXR images, there are complexity in structure and patterns of gray-scale distribution, as well as complex hemidiaphragm borders and details that might interfere with the machine ’s and doctor’s diagnosis. In particular, this paper developed a new multi-scale parallel CNN (called MSP-CNN) for denoising CXR images and a distinctive COVID-19 application that can improve image quality. The foundation of MSP-CNN is comprised of several fundamental parts are: (a) Extracting spatial information with a multi-scale feature extractor (b) Efficient Spatial Channel Attention(ESCA) without raising the network parameters, more attention to be paid to complex structures and hemidiaphragm edges in CXR images. Extensive testing has shown that our MSP-CNN is better at preserving complex structures of features in CXR images. Rigorous experiments also show that MSP-CNN has a positive influence on CXR image classification and COVID-19 case detection from denoised CXR images. It is envisioned that with reliable accuracy, this method can be introduced for clinical practices in the future. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network

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Multimedia Tools and Applications 2024年 1-22页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Cerebral stroke indicates a neurological impairment caused by a localized injury to the central nervous system resulting from a diminished blood supply to the brain. Today, stroke stands as a global menace linked to the premature mortality of millions of people globally. Consequently, it is crucial to simulate how different risk factors impact the incidence of strokes and artificial intelligence is emerging as a suitable tool for achieving this goal. This study aims to construct dependable learning prediction models for stroke illness. The proposed approach can handle the inherent difficulty posed by a significant imbalance in classes, where the number of stroke patients is notably smaller compared to the healthy class. The study also provides a model based on an adaptive neuro-fuzzy inference system logic and convolutional neural networks (CNN) for accurate stroke prediction. An adaptive neuro-fuzzy inference system logic approach is adopted as it incorporates the capabilities of artificial intelligence and fuzzy inference, thereby having the potential to yield superior outcomes. The efficacy of the suggested method is extensively analyzed involving machine and deep learning classifiers and considering metrics relevant to encompass both the capacity for generalization and the accuracy in predicting. The proposed fuzzy-CNN model outperforms with the most considerable accuracy of 98.97% when using the original dataset, resampled dataset, and data imputation. K-fold cross-validation also shows superior results with an average accuracy of 99.6% for five folds. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 IEEE.

关键词： Prediction models

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Design and Implementing of Innovative Joystick for Shooting Games

Design and Implementing of Innovative Joystick for Shooting ...

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2024 IEEE International Conference on Smart systems and Power Management, IC2SPM 2024

作者： Akiki, Charbel Zanety, Jeanpierre El Haidar, Gaby Abou Daou, Roy Abi Zeid Université La Sagesse Faculty of Engineering Polytech Biomedical Engineering Department Furn El Chebbak Lebanon American University of Science and Technology Faculty of Engineering & Computer Science Zahle Lebanon Université La Sagesse Faculty of Engineering Mechatronics Engineering department Furn El Chebbak Beirut Lebanon

ISBN: (纸本)9798331529796

The gaming landscape has undergone significant evolution over the years, with technological advancements consistently transforming how players engage with virtual environments. One pivotal aspect of this evolution lies in the interface between the player and the game - the controller. Traditional controllers, while effective, often present challenges such as high costs and limited life cycles. The proposed device is characterized by its low-cost, accuracy, and customization as it can adhere to the user motion. Shooting games, characterized by dynamic and fast-paced interactions, require precise and responsive control mechanisms for an immersive player experience. Therefore, this paper presents a comprehensive exploration of implementing sensor-based controls in shooting games. The selected sensors, coupled with the Arduino-based microcontroller integration, form the backbone of a system designed for accuracy, responsiveness, and adaptability. By providing players with a tangible connection to the virtual realm, the system aims to redefine the boundaries of player interaction, elevating the game beyond a mere gaming experience to a visceral and captivating journey where the line between player and character becomes delightfully blurred. The challenge lies in seamlessly translating physical gestures into in-game actions, redefining the player's relationship with the digital battlefield. Following a successful implementation, the system was subjected to extensive testing by a group of 20 users, with a total testing time exceeding 15 hours. This thorough evaluation yielded overwhelmingly positive feedback, affirming the system's functionality, reliability, and performance. © 2024 IEEE.

关键词： Life cycle

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A MODIFIED WEAK GALERKIN FINITE ELEMENTMETHOD FOR SINGULARLY PERTURBED PARABOLIC CONVECTION-DIFFUSION-REACTION PROBLEMS

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Journal of Computational Mathematics 2023年第6期41卷 1246-1280页

作者： Suayip Toprakseven Fuzheng Gao Faculty of Engineering Department of Computer ScienceArtvin Coruh UniversityArtvin08100Turkey School of Mathematics Shandong UniversityJinan 250100China

In this work,a modified weak Galerkin finite element method is proposed for solving second order linear parabolic singularly perturbed convection-diffusion *** key feature of the proposed method is to replace the classical gradient and divergence operators by the modified weak gradient and modified divergence operators,*** apply the backward finite difference method in time and the modified weak Galerkin finite element method in space on uniform *** stability analyses are presented for both semi-discrete and fully-discrete modified weak Galerkin finite element *** order of convergences are obtained in suitable *** have achieved the same accuracy with the weak Galerkin method while the degrees of freedom are reduced in our *** numerical examples are presented to support the theoretical *** is theoretically and numerically shown that the method is quite stable.

关键词： The modified weak Galerkin finite element method Backward Euler method Parabolic convection-diffusion problems Error estimates

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