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18th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Lin, Zewei Xu, Huarong Xiamen University of Technology Department of Computer and Information Engineering Xiamen China

ISBN: (纸本)9798331505639

With the increase of crowd monitoring and management needs, dense crowd detection has become an important research direction in the field of computer vision, mainly using target detection methods to detect the position and number of pedestrians in pictures and videos. Aiming at the problem of missed detection and misdetection due to the occlusion between pedestrians or the target pedestrians being too small in dense crowd scenes, this paper proposes an improved detection algorithm CscSCA-YOLOv8 based on YOLOv8. Firstly, the Conv-SPD-Conv module is used instead of each convolutional step and each pooling layer in the trunk and the neck, to prevent the loss of fine-grained information and the less efficient feature representation Learning. Then the SCAMix hybrid attention mechanism is introduced into the YOLOv8 network architecture to utilize the global perception capability of self-attention and capture local features via CoordAttention to improve detection in dense scenes. Experimental results show that this paper's method achieves 74.1% and 46.6% for mAP50 and mAP50-95 on the WiderPerson dataset, respectively, and improves 3.6% and 2.8% compared with the original YOLOv8n. © 2024 IEEE.

关键词： Network architecture

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Advancements in Depression Severity Prediction: A Multi-Modal Machine Learning Approach Integrating MCQs and Audio Data 2

Advancements in Depression Severity Prediction: A Multi-Moda...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Pimpalkar, Shilpa P. Chavan, Anish Avinash Gawali, Sadhana Vijay Dalvi, Shreya Sanjay Department of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350383867

This research presents a machine-learning framework for predicting depression severity by leveraging Multiple-Choice Questions (MCQs) and transcribed audio data. Two distinct data sources are employed, with regression techniques (linear, decision tree, random forest, support vector) applied to both MCQs and audio data. The severity predictions from individual regression models are amalgamated using a weighted average. Additionally, a combined machine learning model integrates classification algorithms (logistic regression, decision tree classification, random forest classification, support vector classification) for both MCQ and audio data. Trained separately, these models contribute to an integrated prediction of depression severity levels-minimal, mild, moderate, and severe. The MCQs utilized originate from the Beck Depression Inventory-2 (BDI-2) questionnaire. This comprehensive approach, encompassing subjective self-report and objective audio indicators, aims to enhance the accuracy of depression severity predictions, offering a valuable tool for early detection and intervention in mental health. The first key component of our approach involves utilizing the structured information derived from the Beck Depression Inventory-2 (BDI-2) questionnaire, which is represented by MCQs. This well-established instrument is widely used for assessing depression, providing a standardized set of questions that capture self-reported symptoms and emotional states. Leveraging the richness of MCQ responses, we employ regression techniques such as linear regression, decision tree regression, random forest regression, and support vector regression to predict the severity level of depression for each participant. © 2024 IEEE.

关键词： Prediction models

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Research on Pedestrian Fall Detection Based on YOLOv8n 18

Research on Pedestrian Fall Detection Based on YOLOv8n

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18th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Xu, HuaRong Liu, Wei Xiamen University of Technology Department of Computer and Information Engineering Xiamen China

ISBN: (纸本)9798331505639

To address the challenges of finding an ideal balance between computational redundancy, accuracy, parameter count, and computational complexity in existing pedestrian fall detection algorithms, this paper proposes a lightweight pedestrian fall detection algorithm based on YOLOv8n. Firstly, a DWR-DRB module is designed to enhance the capabilities of large-kernel convolutional layers, extending the receptive field from the higher layers of the network to extract features, thereby reducing computational load and improving model performance. Secondly, a Deformable Attention mechanism is introduced to adaptively adjust the attention mechanism to focus on critical regions of the image, thereby enhancing the model's ability to recognize and localize targets. Finally., a CA-HSFPN module is constructed to improve the expressiveness of feature maps, Substantially decreasing the number of parameters and the computational complexity of the model, achieving a lightweight model. Experiments conducted on the constructed dataset show that the improved algorithm achieves a 0.7% increase in mape@0.5 compared to the original model, with a significant reduction in parameter count and computational load, with parameters decreasing by 33.2% and computational load decreasing by 16%, while maintaining high detection accuracy. © 2024 IEEE.

关键词： Complex networks

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An Essay on Detailed Performance Assessment Ensemble-Based Predictive Modelling in Network Intrusion Detection Systems

An Essay on Detailed Performance Assessment Ensemble-Based P...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Khajgiwale, Parth International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350374711

Network Intrusion Detection Systems (NIDS) play a critical role in safeguarding computer networks against malicious activities and cyber threats. To improve the accuracy and robustness of NIDS, this research explores the effectiveness of ensemble-based predictive modelling. Ensembles combine multiple individual machine learning models to make collective predictions, potentially outperforming single models. The study involves designing and implementing various ensemble techniques, such as Random Forest, Gradient Boosting, and Stacking, and comparing their performance against individual models using a well-curated NIDS dataset. The research findings demonstrate that ensembles show promising results in enhancing NIDS accuracy, especially in detecting various types of network intrusions. The analysis sheds light on the strengths and limitations of ensemble approaches and provides valuable insights for deploying machine learning ensembles in real-world NIDS scenarios to bolster network security and protect against emerging cyber threats. © 2024 IEEE.

关键词： Network intrusion

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DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species 8

DeepFish: A Comprehensive Exploration of Deep Learning Metho...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Bhand, Aarohi More, Priyanka Gawande, Shreyash Deshmane, Swaraj Bharsakale, Pranjal Bhand, Rahi Sakhare, Sachin Thombare, Vipin Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

The DeepFish exploration aims to develop a web-based application that leverages deep learning algorithms to accurately identify and provide detailed information about various fish species based on user-uploaded images, specifically for the unique fish species in Maharashtra. The system utilizes state-of-the-art computer vision techniques to process the uploaded fish images and employs a trained model to predict the fish species. The paper presents a rigorous comparative analysis of deep learning algorithms, to accurately identify the most efficient model for our application with minimum response time and maximum accuracy for identification. The Fish Prediction website provides users with a brief description and relevant information about the predicted fish species including common habitat, diet, physical characteristics, and consumption status, enhancing their knowledge and understanding of the diverse aquatic life. Overall, it provides an accessible and user-friendly platform for fish identification and information retrieval, benefiting researchers, fishing enthusiasts, and conservationists © 2024 IEEE.

关键词： Invertebrates

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Biomarkers to Predictive Modeling: Leveraging Feature Selection and Ensemble Learning for Ovarian Cancer Prognosis 2024

Biomarkers to Predictive Modeling: Leveraging Feature Select...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Jaya Rathi, Megha Department of Computer Science & Engineering Jaypee Institute of Information Technology India

ISBN: (纸本)9798400709722

Treatment outcomes and patient survival rates are greatly improved by early identification of ovarian cancer. However, to increase diagnostic accuracy, effective predictive modeling is required due to the biomarkers' variability and complexity. This research proposes a complex methodology that combines feature selection and ensemble learning approaches to create a forecast model for the diagnosis of ovarian cancer. Recursive feature elimination (RFE), a thorough feature selection technique, was originally used to identify important biomarkers in order to lower dimensionality and improve model interpretability. We then used ensemble learning techniques, which fuse together several machine learning models, to boost the prognosis's stability and predictive performance. Using a dataset containing clinical and genetic data from ovarian cancer patients, ensemble techniques called Adaboost, Catboost, Random Forest, and Gradient boosting were put into practice and validated. Performance metrics like precision, accuracy, F1-score, recall and AUC-ROC curve were used to measure the techniques' efficacy. The findings show that our approach not only performs better than conventional single-predictor models, with results that are roughly 99% correct, but it also offers important insights into the biomarkers which are most symptomatic of the course of ovarian cancer. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Machine learning based Expert System for Online Admissions 15

Machine learning based Expert System for Online Admissions

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Sasane, Sanika Igade, Aditi Shet, Anish Kandalgaonkar, Vighnesh Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331300579

The web application permits on-line admissions saving the time of geographically scattered college *** permits lowering time in activities, centralized information dealing with and paperless admission with decreased *** improves operational performance and decreases the fee. The college admission device has been advanced so as to automate admission device beginning from the notification to admission *** device used a selection tree primarily based totally category method. Prior to device improvement, six fashions have been created and examined to discover the maximum green version which might later be carried out for improvement of internet-primarily based totally pupil admission screening device. Online Admission screening device is an internet device that may be accessed at some stage in the company and out of doors as nicely with right login furnished. The purpose of this studies paintings is to significantly verify the gathering and processing of pupil admission shape procedure so as to become aware of troubles encountered with inside the device, a good way to layout a brand new device a good way to assist lessen or absolutely remove the diagnosed issues internet. The automatic admission processing device will assist lessen a number of the onerous paintings contain in pupil admission processing, maximized returns for the group offer a pleasant consumer interface users. © Grenze Scientific Society, 2024.

关键词： Expert systems

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