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HFANet: Hierarchical Feature-Enhanced Aggregation Network for Camouflaged Object Detection

Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Zhang, Xinchao Zhu, Hengliang Mao, Guojun Department of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

Camouflaged object detection (COD) aims to identify target objects in complex scenes with extremely high similarity to their surroundings, and has significant applications in military, medical, and other fields. This paper proposes a hierarchical feature-enhanced aggregation network (HFANet) for COD, aiming to address the situations that the target object is highly similar to the background. First, we adopt the pyramid vision Transformer model as the backbone for feature extraction. On top of it, the object-region amplification module and deep interaction guidance module are stacked to enhance the perception of camouflaged objects in complex scenes. Second, an enhanced receptive field module is designed to improve edge perception of camouflaged objects. At last, a multi-scale interactive fusion module is designed by cross-scale connection through adjacent layers, effectively improving the accuracy of COD. The proposed method is evaluated on three challenging datasets: CAMO, CHAMELEON, and COD10K. Evaluation results demonstrate superior performance compared to the state-of-the-art methods. © Shanghai Jiao Tong University 2024.

关键词： Object detection

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A Review on Ensemble Techniques and Its Application on Social Bot Detection 7th

A Review on Ensemble Techniques and Its Application on Socia...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Sharma, Jwala Borah, Samarjeet Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India

ISBN: (纸本)9789819764648

Social media attracts all kinds of activities, including product marketing, celebrity marketing, and also it serves as platform for promoting political agenda. As it is gaining popularity from all various source, it has also attracted spammers and automated accounts that are responsible for spreading the misinformation and influencing the audience. In this context, there is a need to properly classify the social media account as bot account or human account. For classification and detection of social bots, different machine learning, deep learning techniques are implemented. In this paper, we have focused on ensemble technique for classification of social bot. Considering heterogeneous base classifier, such as decision tree, logistic regression and k-neighbor classifier, an ensemble model has been built, that combines the prediction of base classifier, and gives the final prediction. The ensemble approach that has been implemented are, majority voting, random forest and bagging with decision tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bot (Internet)

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in Engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Comprehensive Survey of Post-Quantum Secure Lattice-Based Authentication Schemes for VANETs

Comprehensive Survey of Post-Quantum Secure Lattice-Based Au...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Mishra, Dheerendra Nagar, Manish Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications Bhopal462003 India

ISBN: (纸本)9798331512965

Intelligent Transportation Systems (ITS) rely heavily on Vehicular Ad Hoc Networks (VANETs) to enable secure and reliable communication between vehicles and roadside infrastructure. However, Shor's algorithm poses a significant threat to conventional cryptographic systems. To counteract this threat, post-quantum cryptography offers quantum-resistant solutions capable of providing secure authentication in the face of quantum adversaries. This paper presents a comprehensive survey of recently proposed six post-quantum state-of-the-art authentication protocols designed to meet the security and performance demands of VANETs. The survey identifies key strengths and limitations in existing approaches, providing valuable insights to guide future research in designing robust, efficient, and quantum-resistant authentication mechanisms for next-generation vehicular networks. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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A Methodical Approach to Healthcare Cybersecurity: Handling Current Risks and Advancements 6

A Methodical Approach to Healthcare Cybersecurity: Handling ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Husain Bathushaw, M. Nagasundaram, S. Department of Computer Applications VELS Institute of Science Technology and Advanced Studies Pallavaram Chennai600117 India

ISBN: (纸本)9798331522667

The methods were evaluated using machine learning-driven interruption detection, blockchain-enhanced access control for inconsistency localization, and structured threat insight data in a simulated healthcare environment. With an accuracy range of 0.88-0.94 and lift specified between 0.75 and 1, knowledge values ranging from.86 to.92, and F1 scores between and above, the test results demonstrated that all computations were possible. BACA strengthened access control with blockchain;ADA surpassed expectations in identifying discrepancies;TIAA succeeded in risk insights management;and ML-IDS achieved outstanding results in intrusion detection. A comparison with related research highlights the significance of these algorithms in tackling specific cybersecurity issues in the healthcare sector. Because the proposed algorithms provide a holistic approach to safeguard confidential health information, ensure the validity of assessment models, and strengthen organizations against a range of dynamic cyberthreats, they are pertinent to the expanding discussion about cybersecurity in the healthcare industry. © 2025 IEEE.

关键词： Electronic health record

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Advances in Fuzzy Relation Inequalities and Optimization Techniques 2

Advances in Fuzzy Relation Inequalities and Optimization Tec...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Deo, Udit Jain, Jay Kumar Beg, M.M.S. Department of Mathematics and Computer Applications MANIT Bhopal Bhopal India Department of Computer Engineering Zakir Husain College of Engineering and Technology AMU Aligarh India

ISBN: (纸本)9798331530389

Fuzzy relation inequalities (FRIs) play a crucial role in various optimization and decision-making problems that involve uncertainty and imprecision. The solution of such inequalities has been an active area of research, leading to the development of a wide array of advanced algorithms designed to handle fuzzy constraints in optimization problems. This paper reviews the most prominent algorithms developed for solving fuzzy relation inequalities, focusing on the evolution of methods from traditional linear programming to modern metaheuristic algorithms. We explore linear programming approaches that integrate fuzzy relational constraints using different composition operations, such as max-min, max-Lukasiewicz, and addition-min compositions. The advancements in these methods, particularly in handling large-scale and complex fuzzy systems, are highlighted. Additionally, the paper examines the application of metaheuristics, including Particle Swarm Optimization (PSO), in solving fuzzy optimization problems under relational inequalities. The use of bipolar fuzzy relations in optimization models is also reviewed, emphasizing their ability to model conflicting objectives in decision-making. Moreover, geometric programming, hybrid algorithms combining fuzzy relations with other optimization paradigms, and future trends in integrating machine learning techniques into fuzzy optimization are discussed. The review provides valuable insights into the state-of-the-art algorithms for fuzzy relation inequalities, offering potential directions for future research and applications in dynamic, real-world systems. The proposed methods are crucial for improving decision-making in fields such as healthcare, finance, and intelligent systems, where uncertainty and imprecision are inherent. © 2025 IEEE.

关键词： Heuristic algorithms

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WeaponVision AI: a software for strengthening surveillance through deep learning in real-time automated weapon detection

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International Journal of Information technology (Singapore) 2025年第3期17卷 1717-1727页

作者： Yadav, Pavinder Gupta, Nidhi Sharma, Pawan Kumar Department of Computer Applications National Institute of Technology Haryana Kurukshetra 136119 India Mathematics and Scientific Computing National Institute of Technology HP Hamirpur India

During situations involving dangerous activities, such as armed robbery in public areas, surveillance systems often exhibit delays or inefficiencies in their prompt responding. To obviate the necessity for human involvement, there is a requirement for technology capable of autonomously detecting harmful objects, like firearms in surveillance footages. This article presents WeaponVision AI, an advanced software system that has the ability to accurately identify weapons in live feeds, recorded videos, and images. Moreover, this software has the capability to detect guns even under weak lighting circumstances. The deep learning architecture based on modified YOLOv7 was trained on a vast dataset assortment of 79,558 images of weapons, in developing the WeaponVision AI. The model exhibited satisfactory results following the training phase, achieving significant outcome metrics: a precision rate of 91.75% and a mean average precision of 92.15%. The efficacy of WeaponVision AI is showcased through its ability to accurately identify weapons across diverse environmental and visual conditions. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： computer vision Deep learning Machine learning Security and surveillance Surveillance system Weapon detection

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Creation of Cartoon Face Images of Celebrities: Automation

Creation of Cartoon Face Images of Celebrities: Automation

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Guru, D.S. Shivaprasad, D.L. Prajna, S. Department of Studies in Computer Science University of Mysore Manasagangotri Mysuru India Department of Computer Applications MIT First Grade College Mysuru India Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9783031845420

This paper addresses the problem of automatic detection and identification of celebrity cartoon faces and corpus creation. However, currently available face detection systems are mainly designed to detect human faces. We developed a celebrity car-toon face detector (CCFC) that specially detects celebrity cartoon faces featuring famous individuals, which is customised by a well-known object detector, Yolo-NAS, a highly efficient and lightweight deep architecture. Initially, we train our detector by employing a manually compiled dataset. Also, a suitable matching technique is utilised to create a celebrity cartoon face image database. The performance of the proposed system is evaluated with three different matching thresholds, and 90% accuracy is achieved in database creation. The novelty of the work lies in the complete setup of the process of creating a deployable database since this work is the first of its kind to automate the task of creating a corpus of images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automation Celebrity Cartoon Face Detector Database Creation

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Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations

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SN computer Science 2025年第2期6卷 1-13页

作者： Dangi, Dharmendra Dixit, Dheeraj Kumar Bhagat, Amit Rao, Durgesh Gupta, Jeetendra Kumar Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science and Engineering MITS Madhya Pradesh Gwalior India Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Applications Atal Bihari Vajpayee Vishwavidyalaya Chhattisgarh Bilaspur India

Human motion detection is a critical application of biomechanics, leveraging computer vision to analyse and interpret human activities. This technology is essential across various fields, including sports training, where it aids in monitoring and optimizing athlete performance, and security, where it plays a role in tracking movements in sensitive areas. In this study, we employ two distinct datasets: the Movi dataset and the MoCap CMU dataset. To address the challenge of limited data, we utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for data augmentation, generating new, synthetic images to enhance the diversity and quantity of the training data. Subsequent analysis was conducted using multiple deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), VGG16, AlexNet, and AlexNet with K-Fold Cross Validation. The application of these models yielded promising results, with accuracies of 87.5%, 89.9%, 90.2%, 91.2%, and 98.3%, respectively. The findings demonstrate the effectiveness of DCGAN in improving model performance through data augmentation, as well as the potential of these deep learning models to accurately detect and classify human motion. This research contributes to advancements in biomechanics and its applications in sports science and security domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomechanics DCGAN Deep learning Human motion detection K-Fold cross validation Transfer learning

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Exploring AI Techniques for Yoga Image Classification: A Focus on Machine Learning and Deep Learning 1

Exploring AI Techniques for Yoga Image Classification: A Foc...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Duggal, Anjali Kumar, Satish Tripathi, Pooja Panjab University Department of Computer Science and Applications Chandigarh India Swami Sarvanand Giri Regional Centre Panjab University Department of Computer Science and Applications Hoshiarpur India Inderprastha Engineering College Aktu Department of Information Technology Lucknow India

ISBN: (纸本)9798331529246

Machine Learning (ML) has become essential for developing intelligent systems, providing critical Artificial Intelligence (AI) capabilities. ML involves teaching computers to solve problems by analyzing data, creating analytic models automatically, and performing associated tasks. Deep Learning (DL), a sophisticated branch of ML, utilizes neural networks to simulate AI and often surpasses traditional ML models and data analysis techniques, especially in applications like image classification. This study investigates ML and DL techniques with a particular focus on image classification applications. A novel experiment was conducted using the CNN and VGG16 model, with variations in batch size configurations to optimize yoga posture classification. The methodology involved a comprehensive review of existing approaches and experimental evaluation to assess the model's performance under different configurations. The experimental results demonstrated significant improvements in classification accuracy, achieving up to 86% in recognizing yoga postures. This represents a notable advancement in precision compared to existing methods, showcasing the effectiveness of DL models such as VGG16 in tackling complex image classification challenges. These findings underscore the potential of DL in achieving high accuracy in specialized domains like yoga posture recognition. The study provides a strong foundation for further research and application in related fields, highlighting the efficacy of advanced DL models in addressing domain-specific image classification problems. © 2025 IEEE.

关键词： Adversarial machine learning

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