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检索条件"主题词=detection Algorithms"

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Intelligent Malware detection: Enhancing Accuracy with Recurrent Neural Network

Intelligent Malware Detection: Enhancing Accuracy with Recur...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： C. A. Kandasamy P. Chandru J.K. Kanimozhi E. Loganathan N. Prakash A. Yoganathan Department of Computer Applications K.S.R. College of Engineering Tiruchengode

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Aim: The purpose of this research is to use Artificial Neural Networks (ANN) to create an intelligent malware detection system and evaluate how well it performs in comparison to ClamA V, a conventional antivirus program that relies on signatures. By using Recurrent Neural Networks (RNN) for feature extraction and classification, the study aims to increase detection accuracy. Materials and Methods: Two groups are taken into consideration in this study. Group 1 is the ANN-based intelligent malware detection system, ClamA V which uses with 26 samples. Group 2 is the 26- sample RNN signature-based detection algorithm. With a 95% confidence interval and a threshold of 0.05%, the G Power value is 80%. Both static and dynamic features taken from malware samples are processed by the suggested RNN -based malware detection method. The collection includes labelled To increase convergence, a backpropagation technique with an adaptable learning rate is used to train the ANN model. Results: The ANN- based intelligent malware detection system outperforms the ClamA V system in terms of detection accuracy (0.92 vs. 0.85), precision (0.88 vs. 0.80), recall (0.91 vs. 0.63), and F1-score (0.91 vs. 0.81). While the ClamA V detection accuracy ranges from 55.23% to 78.650/0, the RNN-based system's detection accuracy ranges from 85.42% to 98.76%. At a significance level of 0.0047 and a learning rate of 0.001, the best classification accuracy is obtained. Conclusion: This study shows that the suggested ANN-based malware detection system more especially, the RNN performs noticeably better than the conventional ClamA V system.

关键词： Recurrent neural networks Accuracy Artificial neural networks Feature extraction Market research Malware Distance measurement Pattern recognition Informatics detection algorithms

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Forest Fire Severity detection using AI

Forest Fire Severity Detection using AI

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Pradip Kumar Barik Jay Suthar Raaj Patel Department of ICT Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

Forest fires pose significant threats to ecosystems and contribute to global warming. Assessing fire severity is crucial for effective firefighting and minimizing damage, typically relying on post-fire metrics. This study addresses real-time forest fire severity assessment, crucial for minimizing fire damage. We propose a method combining deep learning and weather data. Fire severity was then estimated by combining fire area (from image analysis), a visual fire distance rating, and the Fire Weather Index (FWI) from weather data. Various optimizing techniques are used on a formulated weighted average equation such as Genetic algorithm, Particle Swarm Optimization, and Quasinewton method (QN) for finding the optimized weights that minimizes the Mean Square Error (MSE) between assigned and calculated severity scores. Interestingly, the FWI received the highest weight, while the fire area received the least, highlighting the importance of weather data alongside image analysis.

关键词： Wildfires Image analysis Forestry Mathematical models Global warming Indexes Artificial intelligence Particle swarm optimization Meteorology Genetic algorithms detection algorithms Artificial intelligence Climate change

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Recent Advances, Challenges and Future Prospects of Multi-Crop Leaf Disease detection Algorithm Using Deep Learning: A Comprehensive Review

Recent Advances, Challenges and Future Prospects of Multi-Cr...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Swapnil P. Bangal Satish R. Jondhale Balasaheb S. Agarkar Sachin V. Chaudhari Shakil A. Shaikh Department of Electronics & Telecommunication Engineering Sanjivani College of Engineering Kopergoan SPPU Pune India Department of Electronics & Computer Engineering Pravara Rural Engineering College Loni SPPU Pune India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Over the past few years, there have been prominent progress in the development of multi-crop leaf disease detection algorithms using deep learning, offering promising solutions to tackle agricultural challenges. This comprehensive review explores the latest advancements in the field, examining the methodologies and models that have become efficient tools for the early diagnosis of disease across a variety of crops. The review not only highlights the successes but also explains the challenges faced by these algorithms, including issues related to data scarcity, model generalization, and real-world implementation. Furthermore, it presents a forward-looking perspective, discussing the future prospects of this technology, emphasizing the potential for its widespread adoption in precision farming and focusing on the demand for more investigation and innovation. By incorporating recent developments, acknowledging existing challenges, and outlining future directions, this review serves as a valuable tool for academics, professionals, and decision-makers who want to use deep learning to identify leaf diseases across several crops.

关键词： Deep learning Precision agriculture Technological innovation Reviews Collaboration Prediction algorithms Real-time systems Stakeholders detection algorithms Diseases

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An Improved Fire detection Algorithm Based on YOLOv5

An Improved Fire Detection Algorithm Based on YOLOv5

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Zhenjie Wu Xiang Wang Gang Wang Chengzhe Jiang Beihua University Jilin China Jilin Institute of Chemical Technology Jilin China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Early warning of fire is crucial for improving survival rates during fire incidents. Fire and smoke detection is an extremely important safety technology that plays a vital role in timely and accurately detecting fires in the surrounding environment to ensure the safety of lives and production environments. To address the challenge of detecting fires in complex scenarios, we propose a YOLO-based algorithm, utilizing YOLOv5 as its foundation, which offers high detection accuracy while remaining lightweight. Our algorithm incorporates the C2f-ODConv module into the network, allowing for dynamic adjustment of the convolution kernel's shape and size based on the input data's features during the convolution process. This adaptability ensures better compatibility with different input data types. Additionally, we introduce the Decoupled head in the detection head section, replacing the original detection head. This modification reduces interference between the classification head and the detection head of the model, thereby enhancing the overall detection accuracy of the model. Experimental results demonstrate that our proposed algorithm achieves a detection accuracy of 94.7% and a recall rate of 93.2%. These impressive results ensure accurate Decoupled head fire detection even in complex situations.

关键词： YOLO Adaptation models Accuracy Convolution Heuristic algorithms Interference Feature extraction Safety Kernel detection algorithms

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IAT-YOLO: A black pig detection model for use in low-light environments

IAT-YOLO: A black pig detection model for use in low-light e...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Mengzhen Huang Lao Li Huanjun Hu Yifan Liu Yuting Yao Rui Song School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the large-scale intelligent pig farming industry, it is crucial to achieve real-time and accurate identification and positioning of each pig. However, the existing mainstream pig detection algorithms are mainly designed for white pigs, and the detection effect of black pigs is poor in low-light environments. In addition, when black pigs gather and adhesion and occlusion occur, the complexity of detection is further increased. Although strong light irradiation in the pigpen can improve the detection accuracy, it will have an adverse effect on the normal growth of pigs. To ensure accurate detection of black pigs in low-light environments without interfering with the normal growth of pigs, this research presents the IAT-YOLO model, derived from YOLOv7. It combines the brightness adaptive module IAT (Illumination-Adaptive-Transformer), the CA (Coordinate Attention mechanism) and the improved SPPCSPC-R module, which can effectively identify and locate the position and number of black pigs. Tests on the low-light black pig dataset reveal that the proposed model attains 96.8% accuracy, 83.8% recall, 91.8% mAP@0.5, and 54.5% mAP@0.5:0.95, which demonstrates higher accuracy and provides technical support for the intelligent detection of black pigs in such environments.

关键词： Smart agriculture Adaptation models Radiation effects Accuracy Computational modeling Object detection Robustness Real-time systems detection algorithms Farming

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Lightweight Pedestrian detection Algorithm Based on Improved YoLov8s

Lightweight Pedestrian Detection Algorithm Based on Improved...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Chenhao Zhang Weidong Li Xiaojuan Li Hebei University of Economics and Business Shijiazhuang China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address the high computational complexity and parameter redundancy in pedestrian detection models, this paper proposes a lightweight YOLOv8s-based algorithm optimized for resource-constrained devices. The improvements include three key components: (1) Integration of RepViT blocks into the backbone network to replace traditional bottlenecks, where the Squeeze- and-Excitation (SE) attention mechanism is selectively removed from shallow layers to reduce redundancy while retaining it in deeper layers for enhanced feature representation. (2) Replacement of standard convolutions with Group Spatial Convolution (GSConv) to minimize parameters and computational costs. (3) Introduction of the Shape-IoU loss function, which incorporates shape and scale alignment to improve bounding box accuracy for irregular pedestrian contours. Experiments on the WiderPerson dataset demonstrate that the proposed model achieves S9.5 % mAP@0.5 and 63.7% mAP@0.5:0.95, comparable to the original YOLOv8s, while reducing parameters and computational effort by 36.6% (to 7.1 MB) and 32.6% (to 19.4 GFLOPs), respectively. The model also outperforms YOLOv5n, YOLOv6n, and YOLOv8n in recall (8O.8%) and precision (S7.2 % ), achieving an optimal balance between efficiency and accuracy. These advancements enable practical deployment in intelligent transportation, surveillance, and edge-computing scenarios.

关键词： Pedestrians Accuracy Shape Computational modeling Surveillance Image edge detection Redundancy Transportation detection algorithms Standards

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A LiDAR Loop Closure detection Algorithm Integrating Multiple Information of Point Cloud

A LiDAR Loop Closure Detection Algorithm Integrating Multipl...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Chuan Zhang Qiang Hou Zhengpeng Lu Xuefen Zhu Bonan Jin Tao Zhang School of Instrument Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In SLAM (Simultaneous Localization and Mapping) systems, the odometry in the front-end performs inter-frame matching in unstructured environments. However, due to the accumulation of errors, the accuracy of localization and mapping deteriorates. Without external observations, the loop closure optimization strategy triggered by the loop closure detection algorithm is a key method for reducing cumulative errors. Therefore, a LiDAR SLAM loop closure detection algorithm integrating multiple information of point cloud is designed. The algorithm focuses on encoding the height and density information of point cloud. Firstly, a kd-tree-based fast screening of loop closure frames is performed. Then, a refined loop closure frame selection based on similarity calculation is conducted, followed by a geometric consistency verification based on the NICP method. Experimental results on an online unstructured dataset demonstrate that the root mean square error (RMSE) of the absolute trajectory error after optimization by the proposed algorithm is reduced to 0.87 meters, showing a performance improvement of approximately 36%, with point cloud mapping results being more accurate and detailed. Moreover, the precision-recall (PR) curve of the proposed algorithm covers the largest area compared to other loop closure detection algorithms such as Scan Context and Iris.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Laser radar Search problems Encoding Trajectory Odometry detection algorithms Optimization

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Non-coherent Signal detection Algorithm Based on Molecular Communication and Its Experimental Verification

Non-coherent Signal Detection Algorithm Based on Molecular C...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Leifu Ren Yitao Hu Xiaoyi Bian Chuwen Lan Jiaxing Wang Guoan Zhao Zhimin Zheng Chunfeng Cui Zehua Gao China Mobile Information Technology Co. Ltd Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China North China Institute of Aerospace Engineering Hebei China China Mobile Research Institute Beijing China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

Demodulation at the receiver is a pivotal process in molecular communication (MC), involving the identification and transformation of transmitted signals to retrieve the original information from the sender. Due to the unique properties of MC, traditional demodulation techniques from electromagnetic communication cannot be directly applied. The demodulation process in MC must account for complex factors such as molecular diffusion, reaction kinetics, and environmental influences, which can affect the receiver's ability to recognize and process signals. Most existing signal detection methods rely on an unbounded model, which does not align with real-world communication scenarios, and are unsuitable for bounded environments. Addressing these issues, this paper proposes a non-coherent signal detection method suitable for bounded models, using pre-smoothing of data to set thresholds for current time slots. Through experimental simulations of the communication process using the molecular communication simulator developed by China Mobile, we analyze the channel capacity and bit error rate (BER). The results demonstrate that our approach significantly reduces the BER and enhances the reliability of MC in bounded environments.

关键词： Analytical models Computational modeling Bit error rate Molecular communication Receivers Reliability Channel models detection algorithms Signal detection Demodulation

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Novel Abnormal Data Mining and detection Algorithm for Intelligent Systems

Novel Abnormal Data Mining and Detection Algorithm for Intel...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Shaomei Xu Yanling Fan Bing Lin Continuing Education Service Center Shandong Institute Of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In intelligent systems, as the amount of data increases, how to analyse data and extract abnormal information is an important task. Based on this, in order to improve the detection efficiency and accuracy of abnormal data in intelligent systems, this study proposes a new and efficient abnormal data mining and detection algorithm. In the initial modelling process, this study introduced the minimum modification cost principle based on cross entropy for modelling. This algorithm optimizes the time series data cleaning process, thereby removing noise data while maintaining the integrity of the data sequence. Then, the extreme gradient boosting algorithm is combined with the Gaussian kernel function to achieve data dimension reduction. This operation can reduce data dimensions and feature redundancy. Finally, the overall anomaly detection operation by integrating the improved Ada boost algorithm with the sampling balance technology can improve the accuracy of the classifier while solving the problem of unbalanced data. The experimental results show that the proposed method is superior to the traditional algorithms in comprehensive indicators.

关键词： Sentiment analysis Accuracy Time series analysis Redundancy Noise Classification algorithms Intelligent systems detection algorithms Kernel Optimization

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Ultrasound Image Guided Robotic Puncture System With Needle Tip detection and Deviation Correction Features

Ultrasound Image Guided Robotic Puncture System With Needle ...

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IEEE International Conference on Robotics and Biomimetics

作者： Jinhang Li Zhengnan Jiang Chenyang Li Baoliang Zhac Peng Zhang Pak Kin Wong Ying Hu Department of Electromechanical Engineering University of Macau Macau China Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Robotic needle insertion with ultrasound image guidance can help improve the accuracy of liver cancer biopsy. Most robotic puncture systems with ultrasound guidance utilize dual-arm configurations, which are less efficient in terms of space utilization. To address this, we have developed a compact ultrasound image guided needle puncture mechanism. It consists of an ultrasound probe to provide image feedback and a serial-parallel mechanism together with a range extender to drive the puncture needle. To achieve accurate puncture in the dynamic environment of tissue deformation and needle bending, a needle tip detection algorithm in ultrasound images with YOLO V8 network is proposed, together with needle axis fitting and Kalman Filter to furtherly improve the accuracy. With image feedback, a closed-loop deviation correction controller with fuzzy control algorithm is developed and tested on the robotic puncture system, achieving 0.83mm accuracy, which can meet the clinical requirement.

关键词： YOLO Ultrasonic imaging Accuracy Heuristic algorithms Fitting Needles Robot sensing systems Kalman filters Probes detection algorithms

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