Статті в журналах: "Optimized detection model"

1

Huang, K. "An Optimized LightGBM Model for Fraud Detection." Journal of Physics: Conference Series 1651 (November 2020): 012111. http://dx.doi.org/10.1088/1742-6596/1651/1/012111.

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Hu, JianSheng, JunJie Ma, Bin Xiao, and Rui Zhang. "Improved Lightweight YOLOv3 model for Target Detection Algorithm." Journal of Physics: Conference Series 2370, no. 1 (November 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2370/1/012029.

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When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created to improve and optimize the model for the data images. YOLOv3, YOLOv3-Shufflenetv2, and tiny-YOLOv3-Shufflenetv2 are trained and tested on the indoor-2022 small target dataset in the Pytorch framework. The experimental findings indicate that in the indoor-2022 dataset. Compared with the single YOLOv3 model for object detection tasks, the fusion improved model used in this article improves the recognition ability of small objects in indoor images, With a 10-fold reduction in model size and a 4-fold increase in detection speed, only results in 1.6% reduction in the mean accuracy (mAP), and the comparison experiments with the current stage of traditional target detection algorithms validate the proposed tiny-YOLOv3-Shufflenetv2 model is verified to be superior and feasible. The optimized model in this article reduces mannequin parameters and model size while additionally ensuring the accuracy and velocity of inspection, and meets the requirements for deployment on indoor mobile robots.

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Boonsim, Noppakun, and Saranya Kanjaruek. "Optimized transfer learning for polyp detection." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, no. 1 (February 18, 2023): 73–81. http://dx.doi.org/10.37936/ecti-cit.2023171.250910.

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Early diagnosis of colorectal cancer focuses on detecting polyps in the colon as early as possible so that patients can have the best chances for success- ful treatment. This research presents the optimized parameters for polyp detection using a deep learning technique. Polyp and non-polyp images are trained on the InceptionResnetV2 model by the Faster Region Con- volutional Neural Networks (Faster R-CNN) framework to identify polyps within the colon images. The proposed method revealed more remarkable results than previous works, precision: 92.9 %, recall: 82.3%, F1-Measure: 87.3%, and F2-Measure: 54.6% on public ETIS-LARIB data set. This detection technique can reduce the chances of missing polyps during a pro- longed clinical inspection and can improve the chances of detecting multiple polyps in colon images.

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Behera, Bibhuti Bhusana, Binod Kumar Pattanayak, and Rajani Kanta Mohanty. "Deep Ensemble Model for Detecting Attacks in Industrial IoT." International Journal of Information Security and Privacy 16, no. 1 (January 1, 2022): 1–29. http://dx.doi.org/10.4018/ijisp.311467.

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In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the optimized BI-LSTM model. The final outcome regarding the presence/absence of attacks in the industrial IoT is portrayed by the optimized BI-LSTM model. Therefore, the weight of BI-LSTM model is fine-tuned using the newly projected hybrid optimization model referred as cat mouse updated slime mould algorithm (CMUSMA). The projected hybrids the concepts of both the standard slime mould algorithm (SMA) and cat and mouse-based optimizer(CMBO), respectively.

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Al-Sarem, Mohammed, Faisal Saeed, Zeyad Ghaleb Al-Mekhlafi, Badiea Abdulkarem Mohammed, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, and Talal Sarheed Alshammari. "An Optimized Stacking Ensemble Model for Phishing Websites Detection." Electronics 10, no. 11 (May 28, 2021): 1285. http://dx.doi.org/10.3390/electronics10111285.

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Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

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Ragab, Mahmoud, Khalid Eljaaly, Maha Farouk S. Sabir, Ehab Bahaudien Ashary, S. M. Abo-Dahab, and E. M. Khalil. "Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model." Computers, Materials & Continua 71, no. 3 (2022): 5751–64. http://dx.doi.org/10.32604/cmc.2022.024658.

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Vasavi, CH, and N. Divya Sruthi. "Detection of Lung Cancer Using Optimized SVM-CNN Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4608–13. http://dx.doi.org/10.22214/ijraset.2023.54496.

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Abstract: Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is critical for effective treatment. Artificial Intelligence (AI) has shown great promise in improving the accuracy and speed of lung cancer detection. In this study, we present a review of recent research on lung cancer detection using AI, including the use of deep learning, and image analysistechnique. Neural networks have always been several a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human’s computational capabilities. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods.

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Ghaleb Al-Mekhlafi, Zeyad, Badiea Abdulkarem Mohammed, Mohammed Al-Sarem, Faisal Saeed, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, and Talal Sarheed Alshammari. "Phishing Websites Detection by Using Optimized Stacking Ensemble Model." Computer Systems Science and Engineering 41, no. 1 (2022): 109–25. http://dx.doi.org/10.32604/csse.2022.020414.

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Moukhafi, Mehdi, Khalid El Yassini, and Bri Seddik. "Intrusions detection using optimized support vector machine." International Journal of Advances in Applied Sciences 9, no. 1 (March 1, 2020): 62. http://dx.doi.org/10.11591/ijaas.v9.i1.pp62-66.

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Computer network technologies are evolving fast and the development of internet technology is more quickly, people more aware of the importance of the network security. Network security is main issue of computing because the number attacks are continuously increasing. For these reasons, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, specially in data mining, have produced a wide variety of machine learning methods, which can be integrated into an IDS. This study proposes a new method of intrusion detection that uses support vector machine optimizing optimizing by a genetic algorithm. to improve the efficiency of detecting known and unknown attacks, we used a Particle Swarm Optimization algorithm to select the most influential features for learning the classification model.

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Feng, Junzhe, Chenhao Yu, Xiaoyi Shi, Zhouzhou Zheng, Liangliang Yang, and Yaohua Hu. "Research on Winter Jujube Object Detection Based on Optimized Yolov5s." Agronomy 13, no. 3 (March 10, 2023): 810. http://dx.doi.org/10.3390/agronomy13030810.

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Winter jujube is a popular fresh fruit in China for its high vitamin C nutritional value and delicious taste. In terms of winter jujube object detection, in machine learning research, small size jujube fruits could not be detected with a high accuracy. Moreover, in deep learning research, due to the large model size of the network and slow detection speed, deployment in embedded devices is limited. In this study, an improved Yolov5s (You Only Look Once version 5 small model) algorithm was proposed in order to achieve quick and precise detection. In the improved Yolov5s algorithm, we decreased the model size and network parameters by reducing the backbone network size of Yolov5s to improve the detection speed. Yolov5s’s neck was replaced with slim-neck, which uses Ghost-Shuffle Convolution (GSConv) and one-time aggregation cross stage partial network module (VoV-GSCSP) to lessen computational and network complexity while maintaining adequate accuracy. Finally, knowledge distillation was used to optimize the improved Yolov5s model to increase generalization and boost overall performance. Experimental results showed that the accuracy of the optimized Yolov5s model outperformed Yolov5s in terms of occlusion and small target fruit discrimination, as well as overall performance. Compared to Yolov5s, the Precision, Recall, mAP (mean average Precision), and F1 values of the optimized Yolov5s model were increased by 4.70%, 1.30%, 1.90%, and 2.90%, respectively. The Model size and Parameters were both reduced significantly by 86.09% and 88.77%, respectively. The experiment results prove that the model that was optimized from Yolov5s can provide a real time and high accuracy small winter jujube fruit detection method for robot harvesting.

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Liu, Yuan, Xiaofeng Wang, and Kaiyu Liu. "Network Anomaly Detection System with Optimized DS Evidence Theory." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/753659.

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Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

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Wei, Weiyi, Wenxia Chen, and Mengyu Xu. "Co-Saliency Detection of RGBD Image Based on Superpixel and Hypergraph." Symmetry 14, no. 11 (November 12, 2022): 2393. http://dx.doi.org/10.3390/sym14112393.

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For the co-saliency detection algorithm of an RGBD image that may have incomplete detection of common salient regions and unclear boundaries, we proposed an improved co-saliency detection method of RGBD images based on superpixels and hypergraphs. First, we optimized the depth map based on edge consistency, and introduced the optimized depth map into the SLIC algorithm to obtain the better superpixel segmentation results of RGBD images. Second, the color features, optimized depth features and global spatial features of superpixels were extracted to construct a weighted hypergraph model to generate saliency maps. Finally, we constructed a weighted hypergraph model for co-saliency detection based on the relationship of color features, global spatial features, optimized depth features and saliency features among images. In addition, in order to verify the impact of the symmetry of the optimized depth information on the co-saliency detection results, we compared the proposed method with two types of models, which included considering depth information and not considering depth information. The experimental results on Cosal150 and Coseg183 datasets showed that our improved algorithm had the advantages of suppressing the background and detecting the integrity of the common salient region, and outperformed other algorithms on the metrics of P-R curve, F-measure and MAE.

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Jain, Rachna, Saurabh Raj Sangwan, Shivam Bachhety, Surbhi Garg, and Yash Upadhyay. "Optimized Model for Cervical Cancer Detection Using Binary Cuckoo Search." Recent Patents on Computer Science 12, no. 4 (August 19, 2019): 293–303. http://dx.doi.org/10.2174/2213275911666181120092223.

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Background:Cervical Cancer is one of the leading causes of deaths among women in India. Accurate and early detection of cancer seems to be a fruitful approach in the diagnosis process. It will be a boon for the medical industry. Prediction of cervical cancer using all the features takes a lot of time and computational resources. Hence, reducing the features and taking only essential features into consideration is an effective solution.Objective:The aim of the research is to identify the relevant features in the classification of cancer and optimize the model. Feature selection increases the accuracy percentage of any classifier. The binary cuckoo search optimization algorithm was applied to explore the important features in the attribute list.Methods:In our research, the performance of the proposed framework has been verified via instigating it with base classifiers such as Random Forest, kernel SVM, Decision Tree and kNN and then evaluated the results with and without Binary Cuckoo Optimization (BCO). The proposed method involves cuckoo search algorithm for selection of optimal feature split points. Cuckoo Search Optimization is a nature stimulated and breeding process of the cuckoo bird’s algorithm to predict best global solution.Results:The results produced only selected features vital for prediction of cancer. In addition, its performance has been paralleled against other factors such as Accuracy, Precision, Recall and Specificity and F-measure.Conclusion:The experimental results show that Decision Tree classifier outperforms all other classifiers with an accuracy of 94.7% increased to 97% after Cuckoo Optimization.

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S, Prabavathi, and Kanmani P. "Plant Leaf Disease Detection and Classification using Optimized CNN Model." International Journal of Recent Technology and Engineering 9, no. 6 (March 30, 2021): 233–38. http://dx.doi.org/10.35940/ijrte.f5572.039621.

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Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.

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Lahasan, Badr, and Hussein Samma. "Optimized Deep Autoencoder Model for Internet of Things Intruder Detection." IEEE Access 10 (2022): 8434–48. http://dx.doi.org/10.1109/access.2022.3144208.

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Jyothi, K. Krishna, and Shilpa Chaudhari. "Optimized neural network model for attack detection in LTE network." Computers & Electrical Engineering 88 (December 2020): 106879. http://dx.doi.org/10.1016/j.compeleceng.2020.106879.

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Li, Wei, Minjun Peng, Yongkuo Liu, Shouyu Cheng, Nan Jiang, and Hang Wang. "Condition Monitoring of Sensors in a NPP Using Optimized PCA." Science and Technology of Nuclear Installations 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/7689305.

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An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

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Sharma, Prerna, Moolchand Sharma, Divij Gupta, and Nimisha Mittal. "Detection of white blood cells using optimized qGWO." Intelligent Decision Technologies 15, no. 1 (March 24, 2021): 141–49. http://dx.doi.org/10.3233/idt-200055.

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This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. White blood cells count in the human body determines the immune system of the human body. A deviation in the count of WBCs from the normal cell count in the human body may indicate an abnormal condition. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. Quantum Grey Wolf algorithm is used to find the minimal set of optimal features from the set of available features to detect the White Blood Cells optimally. As the ordinary Grey Wolf Optimization algorithm also used to find the minimal set of optimal features, but the features selected by qGWO are better in terms of computational time. Further, several classification algorithms such as Support Vector Machine (SVM), Random Forest algorithm, K Nearest Neighbor(KNN) algorithm are applied to the model to predict its accuracy for the selected subset of features after feature selection. The performance of several classifiers is compared, and the model attained the maximum accuracy of 97.8% using KNN with minimum computational time. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy.

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Zhai, Xianxu, Zhihua Huang, Tao Li, Hanzheng Liu, and Siyuan Wang. "YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection." Electronics 12, no. 17 (August 30, 2023): 3664. http://dx.doi.org/10.3390/electronics12173664.

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With the widespread use of UAVs in commercial and industrial applications, UAV detection is receiving increasing attention in areas such as public safety. As a result, object detection techniques for UAVs are also developing rapidly. However, the small size of drones, complex airspace backgrounds, and changing light conditions still pose significant challenges for research in this area. Based on the above problems, this paper proposes a tiny UAV detection method based on the optimized YOLOv8. First, in the detection head component, a high-resolution detection head is added to improve the device’s detection capability for small targets, while the large target detection head and redundant network layers are cut off to effectively reduce the number of network parameters and improve the detection speed of UAV; second, in the feature extraction stage, SPD-Conv is used to extract multi-scale features instead of Conv to reduce the loss of fine-grained information and enhance the model’s feature extraction capability for small targets. Finally, the GAM attention mechanism is introduced in the neck to enhance the model’s fusion of target features and improve the model’s overall performance in detecting UAVs. Relative to the baseline model, our method improves performance by 11.9%, 15.2%, and 9% in terms of P (precision), R (recall), and mAP (mean average precision), respectively. Meanwhile, it reduces the number of parameters and model size by 59.9% and 57.9%, respectively. In addition, our method demonstrates clear advantages in comparison experiments and self-built dataset experiments and is more suitable for engineering deployment and the practical applications of UAV object detection systems.

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Liu, Hongdan, Yan Liu, Bing Li, and Zhigang Qi. "Ship Abnormal Behavior Detection Method Based on Optimized GRU Network." Journal of Marine Science and Engineering 10, no. 2 (February 12, 2022): 249. http://dx.doi.org/10.3390/jmse10020249.

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Ship abnormal behavior detection is an essential part of maritime supervision. It can assist maritime departments to conduct real-time supervision on a certain sea area, avoid ship risks, and improve the efficiency of sea area supervision. Given the problems of complex detection methods, poor detection effectiveness, and low detection accuracy, a Gated Recurrent Unit (GRU) was proposed for ship abnormal behavior detection. Under the premise of introducing the attention mechanism into a GRU, the optimal GRU structure parameters were obtained through the intelligent algorithm to perform deeper feature extraction and train the ship abnormal behavior based on the optimized GRU neural network, so as to realize the detection and recognition of the trajectory data to be measured. Finally, based on the public data set and the trajectory data of the inward and outward ports of ships issued by Nanjing Section, Jiangsu Maritime Bureau, the TensorFlow frame was used to establish an abnormal behavior detection model. The simulation results demonstrated that the abnormal behavior detection model shortened the abnormal detection time. The abnormal behavior detection model used in the detection of ship abnormal behavior enhanced the accuracy and stability of the abnormal behavior identification and verified the validity and superiority of this method.

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Zhu, Xiaohui, Yong Yue, Prudence Wong, Yixin Zhang, and Hao Ding. "Designing an Optimized Water Quality Monitoring Network with Reserved Monitoring Locations." Water 11, no. 4 (April 6, 2019): 713. http://dx.doi.org/10.3390/w11040713.

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The optimized design of water quality monitoring networks can not only minimize the pollution detection time and maximize the detection probability for river systems but also reduce redundant monitoring locations. In addition, it can save investments and costs for building and operating monitoring systems as well as satisfy management requirements. This paper aims to use the beneficial features of multi-objective discrete particle swarm optimization (MODPSO) to optimize the design of water quality monitoring networks. Four optimization objectives: minimum pollution detection time, maximum pollution detection probability, maximum centrality of monitoring locations and reservation of particular monitoring locations, are proposed. To guide the convergence process and keep reserved monitoring locations in the Pareto frontier, we use a binary matrix to denote reserved monitoring locations and develop a new particle initialization procedure as well as discrete functions for updating particle’s velocity and position. The storm water management model (SWMM) is used to model a hypothetical river network which was studied in the literature for comparative analysis of our work. We define three pollution detection thresholds and simulate pollution events respectively to obtain all the pollution detection time for all the potential monitoring locations when a pollution event occurs randomly at any potential monitoring locations. Compared to the results of an enumeration search method, we confirm that our algorithm could obtain the Pareto frontier of optimized monitoring network design, and the reserved monitoring locations are included to satisfy the management requirements. This paper makes fundamental advancements of MODPSO and enables it to optimize the design of water quality monitoring networks with reserved monitoring locations.

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Junos, Mohamad Haniff, Anis Salwa Mohd Khairuddin, Subbiah Thannirmalai, and Mahidzal Dahari. "An optimized YOLO‐based object detection model for crop harvesting system." IET Image Processing 15, no. 9 (March 18, 2021): 2112–25. http://dx.doi.org/10.1049/ipr2.12181.

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Ikram, Sumaiya Thaseen. "Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM." Journal of Computing and Information Technology 24, no. 2 (June 30, 2016): 133–48. http://dx.doi.org/10.20532/cit.2016.1002701.

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Al Bataineh, Ali, Valeria Reyes, Toluwani Olukanni, Majd Khalaf, Amrutaa Vibho, and Rodion Pedyuk. "Advanced Misinformation Detection: A Bi-LSTM Model Optimized by Genetic Algorithms." Electronics 12, no. 15 (July 27, 2023): 3250. http://dx.doi.org/10.3390/electronics12153250.

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The proliferation of misinformation, as insidious and pervasive as water, presents an unprecedented challenge to public discourse and comprehension. Often propagated to further specific ideologies or political objectives, misinformation not only misleads the populace but also fuels online advertising revenue generation. As such, the urgent need to pinpoint and eliminate misinformation from digital platforms has never been more critical. In response to this dilemma, this paper proposes a solution built on the backbone of massive data generation in today’s digital landscape. By leveraging advanced technologies, such as AI-driven systems with deep learning models and natural language processing capabilities, we can monitor and analyze an extensive scope of social media data. This, in turn, facilitates the identification of misinformation across multiple platforms and alerts users to potential propaganda. Central to our study is the development of misinformation classifiers based on a deep bi-directional long short-term memory (Bi-LSTM) model. This model is further enhanced by employing a genetic algorithm (GA), which automates the search for an optimal neural architecture, thereby significantly impacting the training behavior of the deep learning algorithm and the performance of the model being trained. To validate our approach, we compared the efficacy of our proposed model with nine traditional machine learning algorithms and a deep learning model rooted in long short-term memory (LSTM). The results affirmed the superiority of our GA-tuned Bi-LSTM model, which outperformed all other models in detecting misinformation with remarkable accuracy. Our intention with this paper is not to present our model as a comprehensive solution to misinformation but rather as a technological tool that can aid in the process, supplementing and bolstering the existing methodologies in the field of misinformation detection.

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Körez, Atakan, Necaattin Barışçı, Aydın Çetin, and Uçman Ergün. "Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images." ISPRS International Journal of Geo-Information 9, no. 6 (June 4, 2020): 370. http://dx.doi.org/10.3390/ijgi9060370.

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The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).

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Yang, Xiaoying, Nannan Liang, Wei Zhou, and Hongmei Lu. "A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm." Traitement du Signal 37, no. 6 (December 31, 2020): 929–37. http://dx.doi.org/10.18280/ts.370606.

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This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

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Chen, Dong-Hao, Yu-Dong Cao, and Jia Yan. "Towards Pedestrian Target Detection with Optimized Mask R-CNN." Complexity 2020 (December 22, 2020): 1–8. http://dx.doi.org/10.1155/2020/6662603.

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Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms.

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Zhao, Jian Hua. "Intrusion Detection Engineering Management Based on Optimization of BP Neural Network." Applied Mechanics and Materials 416-417 (September 2013): 1228–32. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1228.

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In order to short the modelling time of BP neural network, this paper designs a kind of genetic algorithm to optimize it. By encoding the individual components, initializing the number of populations, and designing proper fitness function, a binary coding genetic algorithm is provided for BP neural network. And it is used to optimize input variables of BP neural network and reduce its dimension. The experiment is carried out based on KDD Cup 99 data set. The results show that the optimized model has shorter modelling time.

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Shin, Sam-Shin, Seung-Goo Ji, and Sung-Sam Hong. "A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection." Applied Sciences 12, no. 23 (November 25, 2022): 12070. http://dx.doi.org/10.3390/app122312070.

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The growing dependence on digital systems has heightened the risks posed by cybersecurity threats. This paper proposes a new method for detecting malicious webpages among several adversary activities. As shown in previous studies, malicious URL detection performance is significantly affected by the learning dataset features. The overall performance of different machine learning models varies depending on the data features, and using a particular model alone is not always desirable in any given environment. To address these limitations, we propose an ensemble approach using different machine learning models. Our proposed method outperforms the existing single model by 6%, allowing for the detection of an additional 141 malicious URLs. In this study, repetitive tasks are automated, improving the performance of different machine learning models. In addition, the proposed framework builds an advanced feature set based on URL and web content and includes the most optimized detection model structure. The proposed technology can contribute to define an advanced feature set based on URL and web content and includes the most optimized detection model structure and research on automated technology for the detection of malicious websites, such as phishing websites and malicious code distribution.

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30

Park, Jungme, Pawan Aryal, Sai Rithvick Mandumula, and Ritwik Prasad Asolkar. "An Optimized DNN Model for Real-Time Inferencing on an Embedded Device." Sensors 23, no. 8 (April 14, 2023): 3992. http://dx.doi.org/10.3390/s23083992.

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For many automotive functionalities in Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD), target objects are detected using state-of-the-art Deep Neural Network (DNN) technologies. However, the main challenge of recent DNN-based object detection is that it requires high computational costs. This requirement makes it challenging to deploy the DNN-based system on a vehicle for real-time inferencing. The low response time and high accuracy of automotive applications are critical factors when the system is deployed in real time. In this paper, the authors focus on deploying the computer-vision-based object detection system on the real-time service for automotive applications. First, five different vehicle detection systems are developed using transfer learning technology, which utilizes the pre-trained DNN model. The best performing DNN model showed improvements of 7.1% in Precision, 10.8% in Recall, and 8.93% in F1 score compared to the original YOLOv3 model. The developed DNN model was optimized by fusing layers horizontally and vertically to deploy it in the in-vehicle computing device. Finally, the optimized DNN model is deployed on the embedded in-vehicle computing device to run the program in real-time. Through optimization, the optimized DNN model can run 35.082 fps (frames per second) on the NVIDIA Jetson AGA, 19.385 times faster than the unoptimized DNN model. The experimental results demonstrate that the optimized transferred DNN model achieved higher accuracy and faster processing time for vehicle detection, which is vital for deploying the ADAS system.

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Yan, Hongwen, Songrui Cai, Qiangsheng Li, Feng Tian, Sitong Kan, and Meimeng Wang. "Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments." Plants 12, no. 9 (April 26, 2023): 1769. http://dx.doi.org/10.3390/plants12091769.

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Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model’s performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily.

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Sun, Guangming, Shuo Wang, and Jiangjian Xie. "An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5." Electronics 12, no. 7 (March 23, 2023): 1515. http://dx.doi.org/10.3390/electronics12071515.

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As one of the more difficult problems in the field of computer vision, utilizing object image detection technology in a complex environment includes other key technologies, such as pattern recognition, artificial intelligence, and digital image processing. However, because an environment can be complex, changeable, highly different, and easily confused with the target, the target is easily affected by other factors, such as insufficient light, partial occlusion, background interference, etc., making the detection of multiple targets extremely difficult and the robustness of the algorithm low. How to make full use of the rich spatial information and deep texture information in an image to accurately identify the target type and location is an urgent problem to be solved. The emergence of deep neural networks provides an effective way for image feature extraction and full utilization. By aiming at the above problems, this paper proposes an object detection model based on the mixed attention mechanism optimization of YOLOv5 (MAO-YOLOv5). The proposed method fuses the local features and global features in an image so as to better enrich the expression ability of the feature map and more effectively detect objects with large differences in size within the image. Then, the attention mechanism is added to the feature map to weigh each channel, enhance the key features, remove the redundant features, and improve the recognition ability of the feature network towards the target object and background. The results show that the proposed network model has higher precision and a faster running speed and can perform better in object-detection tasks.

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33

YeaSul Kim, YeaSul Kim, YeEun Kim YeaSul Kim, and Hwankuk Kim YeEun Kim. "A Comparison Experiment of Binary Classification for Detecting the GTP Encapsulated IoT DDoS Traffics in 5G Network." 網際網路技術學刊 23, no. 5 (September 2022): 1049–60. http://dx.doi.org/10.53106/160792642022092305013.

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5G is characterized by ultra-low latency and the deployment of large-scale IoT environments. IoT devices with weak security can cause security problems such as network failures in 5G. To solve this problem, automated intrusion detection research using ML was being conducted. In previous studies, detection research using ML in the wired network environment was active, but it was relatively insufficient in the 5G network. In addition, the vast amount of traffic in IoT devices can create latency problems for intrusion detection with ML, making it difficult to achieve ultra-low latency 5G service objectives. Therefore, this study analyzed the meaning of the performance and required time of an optimized single ML model and ensemble learning experiment to detect in real time while ensuring high detection performance of large-capacity DDoS in 5G network. When the 5G GTP encapsulated traffic was collected and binary classification was performed, the optimized single ML model performed more than 99%. Especially, compared with ensemble learning, the experimental results showed similar performance and reduced detection time by at least 34 times. As a result of the experiment, it was shown that a single ML model optimized for detecting IoT DDoS in 5G with ultra-low latency is significant.

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34

Zhang, Shuo, Yanxia Wu, Chaoguang Men, Ning Ren, and Xiaosong Li. "Channel Compression Optimization Oriented Bus Passenger Object Detection." Mathematical Problems in Engineering 2020 (March 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/3278235.

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Bus passenger flow information can facilitate scientific dispatching plans, which is essential to decision making and operation performance evaluation. Real-time acquisition of bus passenger flow information is an indispensable part for bus intellectualization. The method of passenger flow statistics in bus video monitoring scene based on deep convolution neural network can provide rich information for passenger flow statistics. In order to adapt to the real scenario of mobile and embedded devices on buses, and to consider the bandwidth limitation, this paper uses a lightweight network model M7, which is suitable for the vehicle system. Based on the classic network model tiny YOLO, the model is optimized by a depthwise separable convolution method. The optimized network model M7 reduces the number of parameters and improves the detection speed, while maintaining a low loss in detection accuracy. As such, the network model M7 is compressed and further optimized by removing redundant channels. The experimental results show that the detection speed of the network model target recognition after channel compression is 40%, which is faster than the precious channel compression on the premise of ensuring detection.

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35

Tariq, Rizwan, Ibrahim Alhamrouni, Ateeq Ur Rehman, Elsayed Tag Eldin, Muhammad Shafiq, Nivin A. Ghamry, and Habib Hamam. "An Optimized Solution for Fault Detection and Location in Underground Cables Based on Traveling Waves." Energies 15, no. 17 (September 5, 2022): 6468. http://dx.doi.org/10.3390/en15176468.

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Faults in the power system affect the reliability, safety, and stability. Power-distribution systems are familiar with the different faults that can damage the overall performance of the entire system, from which they need to be effectively cleared. Underground power systems are more complex and require extra accuracy in fault detection and location for optimum fault management. Slow processing and the unavailability of a protection zone for relay coordination are concerns in fault detection and location, as these reduce the performance of power-protection systems. In this regard, this article proposes an optimized solution for a fault detection and location framework for underground cables based on a discrete wavelet transform (DWT). The proposed model supports area detection, the identification of faulty sections, and fault location. To overcome the abovementioned facts, we optimize the relay coordination for the overcurrent and timing relays. The proposed protection zone has two sequential stages for the current and time at which it optimizes the current and time settings of the connected relays through Newton–Raphson analysis (NRA). Moreover, the traveling times for the DWT are modeled, which relate to the protection zone provided by the relay coordination, and the faulty line that is identified as the relay protection is not overlapped. The model was tested for 132 kV/11 kV and 16-node networks for underground cables, and the obtained results show that the proposed model can detect and locate the cable’s faults speedily, as it detects the fault in 0.01 s, and at the accurate location. MATLAB/Simulink (DigSILENT Toolbox) is used to establish the underground network for fault location and detection.

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36

Xue, Panpan, Wenjin Hu, and Guoyuan He. "Few Shot Object Detection for Tangka Seats Based on Deformable Convolution." Journal of Physics: Conference Series 2221, no. 1 (May 1, 2022): 012053. http://dx.doi.org/10.1088/1742-6596/2221/1/012053.

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Abstract According to deformable target sizes in the detection for Thangka Seats, an optimized few shot Thangka detection model is proposed. The deep residual network based on the deformable convolution is designed to extract the Thangka image features to decrease the missing detections rate. Experimental results show that the proposed method has better performance than the FSOD model on the Thangka dataset. The AP of 2-way 1-shot is 28.5 %, and the AP50 reaches 67.6 %, which increases by 4.7 % and 4.7 %, respectively.

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Kim, Byunghyun, and Soojin Cho. "Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model." Applied Sciences 10, no. 22 (November 12, 2020): 8008. http://dx.doi.org/10.3390/app10228008.

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In many developed countries with a long history of urbanization, there is an increasing need for automated computer vision (CV)-based inspection to replace conventional labor-intensive visual inspection. This paper proposes a technique for the automated detection of multiple concrete damage based on a state-of-the-art deep learning framework, Mask R-CNN, developed for instance segmentation. The structure of Mask R-CNN, which consists of three stages (region proposal, classification, and segmentation) is optimized for multiple concrete damage detection. The optimized Mask R-CNN is trained with 765 concrete images including cracks, efflorescence, rebar exposure, and spalling. The performance of the trained Mask R-CNN is evaluated with 25 actual test images containing damage as well as environmental objects. Two types of metrics are proposed to measure localization and segmentation performance. On average, 90.41% precision and 90.81% recall are achieved for localization and 87.24% precision and 87.58% recall for segmentation, which indicates the excellent field applicability of the trained Mask R-CNN. This paper also qualitatively discusses the test results by explaining that the architecture of Mask R-CNN that is optimized for general object detection purposes, can be modified to detect long and slender shapes of cracks, rebar exposure, and efflorescence in further research.

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38

Xian, Ning. "Comparative study of swarm intelligence-based saliency computation." International Journal of Intelligent Computing and Cybernetics 10, no. 3 (August 14, 2017): 348–61. http://dx.doi.org/10.1108/ijicc-03-2017-0024.

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Purpose The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection. Design/methodology/approach To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation. Findings According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms. Practical implications The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images. Originality/value CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.

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39

Yang, Tianhe, Kai Zhou, Lei Jin, Rui Liu, and Weigen Chen. "Optimization of Photoacoustic Cell for Trace Acetylene Detection in Transformer Oil." Atmosphere 14, no. 5 (April 28, 2023): 801. http://dx.doi.org/10.3390/atmos14050801.

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This paper presents the development of a highly sensitive gas detection system based on a resonant photoacoustic cell for detecting dissolved gases in transformer oil. A simulation model of the resonant photoacoustic cell was studied and optimized the buffer chamber volume while ensuring signal enhancement. The volume of the photoacoustic cell was reduced by about 80% compared to the classical model. A resonant photoacoustic cell was then fabricated based on the optimized simulation optimization. The dual-resonance photoacoustic system was constructed by combining the resonant PA cell with a handmade cantilever fiber acoustic sensor. The system’s sensitivity was further improved by using an erbium-doped fiber amplifier, wavelength modulation, and harmonic detection technology. The experimental results showed that the system achieved a detection limit of 6 ppb and an excellent linear range under 1000 ppm for C2H2 gas. The developed gas detection system has potential applications for monitoring the condition of power transformers in power grids.

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Deshpande, Apoorva, and Ramnaresh Sharma. "Anomaly Detection using Optimized Features using Genetic Algorithm and MultiEnsemble Classifier." IJOSTHE 5, no. 6 (December 28, 2018): 7. http://dx.doi.org/10.24113/ojssports.v5i6.79.

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Anomaly detection system plays an important role in network security. Anomaly detection or intrusion detection model is a predictive model used to predict the network data traffic as normal or intrusion. Machine Learning algorithms are used to build accurate models for clustering, classification and prediction. In this paper classification and predictive models for intrusion detection are built by using machine learning classification algorithms namely Random Forest. These algorithms are tested with KDD-99 data set. In this research work the model for anomaly detection is based on normalized reduced feature and multilevel ensemble classifier. The work is performed in divided into two stages. In the first stage data is normalized using mean normalization. In second stage genetic algorithm is used to reduce number of features and further multilevel ensemble classifier is used for classification of data into different attack groups. From result analysis it is analysed that with reduced feature intrusion can be classified more efficiently.

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41

Balambigai, S., K. Elavarasi, M. Abarna, R. Abinaya, and N. Arun Vignesh. "Detection and optimization of skin cancer using deep learning." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012040. http://dx.doi.org/10.1088/1742-6596/2318/1/012040.

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Abstract Convolutional Neural Network (CNN) is a branch of deep learning which has been one of a popular methods in different applications, especially in medical field. In this study, an optimized CNN model is built using the random search optimization to classify seven types of skin cancer, namely, basal cell carcinoma, melanoma, dermatofibroma, vascular lesion, melanocytic nevus, actinic keratosis and benign keratosis. Total of 10,015 images were collected from the Human Against Machine dataset (HAM10000) which is available in Kaggle, Even though CNN has shown best results in many applications, the hyper-parameters that are required to build CNN model is difficult to choose. If the chosen hyper-parameters doesn’t show good results, the model should be trained again with other set of hyper-parameter values. To avoid this circumstance, the hyper-parameter optimization is required and in this study, it is done using random search optimization. A base CNN model is initially created without using any optimization technique, so that the performance of the CNN model which is optimized by the random search method can be compared and analysed. The first model provided an accuracy of 73.34%, whereas the optimized model shown an improvement in accuracy of 77.17%.

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Li, Zhenhui, Xiaoping Zhuang, Haibo Wang, Yong Nie, and Jianzhong Tang. "Local Attention Sequence Model for Video Object Detection." Applied Sciences 11, no. 10 (May 17, 2021): 4561. http://dx.doi.org/10.3390/app11104561.

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Video object detection still faces several difficulties and challenges. For example, the imbalance of positive and negative samples leads to low information processing efficiency, and detection performance declines in abnormal situations in video. This paper examines video object detection based on local attention to address such challenges. We propose a local attention sequence model and optimized the parameter and calculation of ConvGRU. It could process spatial and temporal information in videos more efficiently and ultimately improve detection performance under abnormal conditions. The experiments on ImageNet VID show that our method could improve the detection accuracy by 5.3%, and the visualization results show that the method is adaptive to different abnormal conditions, thereby improving the reliability of video object detection.

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Yen, Chih-Huang, Pin-Yuan Huang, and Po-Kai Yang. "An Intelligent Model for Facial Skin Colour Detection." International Journal of Optics 2020 (March 17, 2020): 1–8. http://dx.doi.org/10.1155/2020/1519205.

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There is little research on the facial colour; for example, choice of cosmetics usually was focused on fashion or impulse purchasing. People never try to make right decision with facial colour. Meanwhile, facial colour can be also a method for health or disease prevention. This research puts forward one set of intelligent skin colour collection method based on human facial identification. Firstly, it adopts colour photos on the facial part and then implements facial position setting of the face in the image through FACE++ as the human facial identification result. Also, it finds out the human face collection skin colour point through facial features of the human face. The author created an SCE program to collect facial colour by each photo, and established a hypothesis that uses minima captured points assumption to calculate efficiently. Secondly, it implements assumption demonstration through the Taguchi method of quality improvement, which optimized six point skin acquisition point and uses average to calculate the representative skin colour on the facial part. It is completed through the Gaussian distribution standard difference and CIE 2000 colour difference formula and uses this related theory to construct the optimized program FaceRGB. This study can be popularized to cosmetics purchasing and expand to analysis of the facial group after big data are applied. The intelligent model can quickly and efficiently to capture skin colour; it will be the basic work for the future fashion application with big data.

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44

Wang, Haiqing, Shuqi Shang, Dongwei Wang, Xiaoning He, Kai Feng, and Hao Zhu. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model." Agriculture 12, no. 7 (June 27, 2022): 931. http://dx.doi.org/10.3390/agriculture12070931.

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Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

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45

Ch, Anusha, Rupa Ch, Samhitha Gadamsetty, Celestine Iwendi, Thippa Reddy Gadekallu, and Imed Ben Dhaou. "ECDSA-Based Water Bodies Prediction from Satellite Images with UNet." Water 14, no. 14 (July 15, 2022): 2234. http://dx.doi.org/10.3390/w14142234.

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The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow to detect the water bodies. It uses a Nadam optimizer to reduce the losses. It also finds best-optimized parameters for the activation function, a number of nodes in each layer. This proposed model achieves integrity by embedding a security feature Elliptic Curve Digital Signature Algorithm (ECDSA). It generates a digital signature for the predicted area of water bodies which helps to secure the key and the detected water bodies while transmitting in a channel. Thus, the proposed model ensures the performance accuracy of 94% which can also work the same for edge detection, detection in blurred and low-resolution images. The model is highly robust.

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A. Malibari, Areej, Marwa Obayya, Mohamed K. Nour, Amal S. Mehanna, Manar Ahmed Hamza, Abu Sarwar Zamani, Ishfaq Yaseen, and Abdelwahed Motwakel. "Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection." Computers, Materials & Continua 73, no. 2 (2022): 4123–38. http://dx.doi.org/10.32604/cmc.2022.030492.

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47

Prakash, A., and C. Chandrasekar. "An Optimized Multiple Semi-Hidden Markov Model for Credit Card Fraud Detection." Indian Journal of Science and Technology 8, no. 2 (January 1, 2015): 165. http://dx.doi.org/10.17485/ijst/2015/v8i2/58081.

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48

Westland, Stephen, and David H. Foster. "Optimized model of oriented-line-target detection using vertical and horizontal filters." Journal of the Optical Society of America A 12, no. 8 (August 1, 1995): 1617. http://dx.doi.org/10.1364/josaa.12.001617.

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49

Shetty, Savita, and Annapurna P. Patil. "Oral cancer detection model in distributed cloud environment via optimized ensemble technique." Biomedical Signal Processing and Control 81 (March 2023): 104311. http://dx.doi.org/10.1016/j.bspc.2022.104311.

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50

Kong, Shuolin, Jian Li, Yuting Zhai, Zhiyuan Gao, Yang Zhou, and Yanlei Xu. "Real-Time Detection of Crops with Dense Planting Using Deep Learning at Seedling Stage." Agronomy 13, no. 6 (May 30, 2023): 1503. http://dx.doi.org/10.3390/agronomy13061503.

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Crop seedlings are similar in appearance to weeds, making crop detection extremely difficult. To solve the problem of detecting crop seedlings in complex field environments, a seedling dataset with four crops was constructed in this study. The single leaf labeling method was proposed as an alternative to conventional labeling approaches to improve the detection accuracy for dense planting crops. Second, a seedling detection network based on YOLOv5 and a transformer mechanism was proposed, and the effects of three features (query, key and value) in the transformer mechanism on the detection accuracy were explored in detail. Finally, the seedling detection network was optimized into a lightweight network. The experimental results show that application of the single leaf labeling method could improve the mAP0.5 of the model by 1.2% and effectively solve the problem of missed detection. By adding the transformer mechanism module, the mAP0.5 was improved by 1.5%, enhancing the detection capability of the model for dense and obscured targets. In the end, this study found that query features had the least impact on the transformer mechanism, and the optimized model improved the computation speed by 23 ms·frame−1 on the intelligent computing platform Jetson TX2, providing a theoretical basis and technical support for real-time seedling management.

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