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Enhanced Object Detection and Classification via Multi-Method Fusion

computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Transfer Learning based Framework of VGG16 to Detect Breast Cancer using Histopathological Images 6

Transfer Learning based Framework of VGG16 to Detect Breast ...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Divya, Zion G. Tripathy, B.K. School of Computer Science and Engineering VIT TN Vellore632014 India School of Computer Science Engineering and Information Systems VIT TN Vellore632014 India

Breast Cancer Detection introduces a prominent confrontation for researchers and clinical experts as it is one of the major public health issues and is weighed as a leading root for cancer correlated deaths among women worldwide. Early diagnosis helps the chances of survival rate which is a crucial part, but standard breast cancer classical techniques rely on operative techniques, open procedures and time-consuming analyses. So, the standard classical techniques pave a way in demanding accurate solutions that are provided with efficient algorithms. This paper addresses these challenges by utilizing automated cancer detection that presents an ensemble deep learning approach by implementing transfer learning for breast cancer detection using histopathological images for classifying Benign and Malignant tissues. Transfer learning uses VGG16 to train the model on large dataset, by reusing the knowledge that has obtained from previous task which is considered as an input to another task thereby improving the performance. In this paper VGG16 architecture is considered as a pretrained model to train on ImageNet. Ensemble strategy is applied as a next step by taking the average of predicted probabilities, out of which the VGG16 model offers overall best accuracy of 98.83%. © 2025 Copyright for this paper by its authors.

关键词： CNN Deep Learning Transfer Learning VGG16

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Vet the Pet: Design of an Intelligent Health Prediction System and Health Care Application for Furry Friends in Saudi Arabia 2

Vet the Pet: Design of an Intelligent Health Prediction Syst...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Gull, Hina Alharbi, Asma Abdulrahman Alahmari, Lama Abdullah Alghamdi, Roaa Saleh Nasser, Salma Sami Alqahtani, Samar Saeed Iqbal, Sardar Zafar Krishna, Gomathi Alnasser, Maryam Abdullah Imam Abdulrahman Bin Faisal University College of Computer Science and Information Technology Department of Computer Information Systems P.O Box 1982 Dammam31441 Saudi Arabia

ISBN: (纸本)9798350371314

Worldwide pet adoption rates are rising, and with them is the demand for pet healthcare services, especially in Saudi Arabia (KSA). To address this growing requirement, there don't seem to be many comprehensive and user-friendly solutions available. In this paper, we present the architecture of an intelligent healthcare application for pets. We have carried out pilot research in Saudi Arabia to ascertain the issues encountered by pet owners. The Vet the Pet app has been suggested in response to the issues that have been found. The Vet the Pet app will function as a pet health prediction system by providing technological solutions. It will help not only in anticipating health-related problems in pets but also in managing them with additional technological features. This application will make it simple for pet owners to communicate with licensed veterinarians via their smartphones, keeping them away from the need for busy clinics and lengthy wait periods. 'Vet the Pet' enables pet owners to easily schedule veterinary visits, access virtual consultations, and get prompt and accurate advice for pet emergencies using smartphone-enabled mobile health technologies. The 'Vet the Pet' smartphone app seeks to meet the growing need for prompt and simple pet medical care. Consequently, we have developed a model of the app that encompasses all of its features. © 2024 IEEE.

关键词： Smartphones

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Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

Usefulness of Honeypots Towards Data Security: A Systematic ...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Silaen, Kalpin Erlangga Meyliana, Meyliana Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Hidayanto, Achmad Nizar Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Information System Depok Indonesia

ISBN: (纸本)9798350382914

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity. © 2023 IEEE.

关键词： Machine learning

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Building an Adaptive Cooperative Transmission Mechanism for Multimedia Convergence System

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Wireless Personal Communications 2023年第4期133卷 2227-2266页

作者： Su, Ming-Kai Lin, Jia-Yi Horng, Gwo-Jiun Aripriharta, Aripriharta Chen, Chao-Chun Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan Taiwan Malang Indonesia

In people's daily lives, electronic mail, audio-visual media platforms, and communication software all have the need for file transmission. This article proposes a digital aggregation system with an adaptive collaborative transmission mechanism. By using traditional UDP and TCP transmission protocols, the file transmission method is adaptively adjusted based on the file type, file size, and network conditions of the device. Through these selection parameters, the file has the most suitable and optimized transmission and upload mode during the transmission process. This article proposes a transmission mode that combines transmission speed and file integrity, systematically examining the existing transmission environment and improving traditional transmission protocols. The communication protocol method proposed through this method is 2.5% faster than traditional transmission protocols for image file transmission. Compared to traditional transmission protocols, the transmission speed of audio files is 1.07% faster. Compared to traditional transmission protocols, video file transmission speed is 1.21% faster. This study adopts a collaborative communication transmission mechanism, which accelerates the transmission speed of multiple clients obtaining files from the server by 1.2% compared to a non-collaborative communication transmission mechanism. The client can still connect to the server at a slower time. This mechanism can accelerate download speed and make it easier to obtain files. Combining with the transmission platform, examining the environmental conditions of the device can be applied to optimize the transmission between servers and clients. As users grow, cooperative communication can be used to accelerate the speed of file acquisition. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cooperative communication

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Postural Sway Detection using Kolmogorov-Arnold Network as Siamese Model

Postural Sway Detection using Kolmogorov-Arnold Network as S...

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2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era, ISSI 2024

作者： Nehary, Ebrahim A. Rajan, Sreeraman Ando, Bruno Carleton University Systems and Computer Engineering Ottawa Canada University of Catania Electric Electronic and Information Engineering Catania Italy

ISBN: (纸本)9798350378689

Continuous monitoring of postural sway is crucial for safeguarding elderly individuals and patients with neurological conditions, such as Parkinson's disease, as their balance is significantly impacted. This continuous monitoring is achieved using triaxial accelerometer sensors that provide time series signals and are used to train models to detect the postural sway of elderly individuals or patients with neurological conditions. However, the available postural sway dataset has a limited number of samples, and the performance of trained models deteriorates when the accelerometer signal is noisy. A Siamese network Kolmogorov-Arnold Network (Siamese-KAN) is pro-posed to address this issue. This network can be trained with a few samples from each class. Training of the Siamese network is conducted by flattening the bispectrum of each accelerometer channel and then fusing the magnitudes to construct a single input vector. Various similarity functions are employed along with contrastive loss to train the Siamese network. Additionally, a Siamese network with multi-layer perceptron (MLP) is also similarly trained for comparison purposes. Preliminary results show that the Siamese-KAN model achieves better accuracy with clean signals than the Siamese-MLP. However, when the signal is noisy, the Siamese-KAN model outperforms the Siamese-MLP using Euclidean and Manhattan similarity functions, while the Siamese-MLP performs better with Cosine and Bray-Curtis similarity functions. © 2024 IEEE.

关键词： Accelerometers

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Mild-NMS: A Suppression Algorithm for Enhanced Object Detection and Classification for High Intra-Class Overlap Ranges 6

Mild-NMS: A Suppression Algorithm for Enhanced Object Detect...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Oladive, Kian Angelo Alampay, Raphael Abu, Patricia Angela Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines Quezon City Philippines

ISBN: (纸本)9798331518158

Greedy Non-Maximum Suppression (NMS) is a necessity in the object detection pipelines of state-of-the-art models. Because this greedy heuristic can often lead to false negatives when more overlap or crowdedness is introduced into an image, Soft-NMS was created to handle the suppression less aggressively. Soft-NMS, however, can have the same effect as Greedy NMS when more crowdedness is introduced due to its repeated decaying of confidence scores. To this end, Mild-NMS is proposed as a means to handle higher overlap scenarios where Soft-NMS might perform like Greedy NMS and produce false negatives. Its idea is to limit the number of times a prediction's confidence score can be decayed by Soft-NMS to only one. Improvements in the performance of a stock YOLOV8x model pre-trained on the Common Objects in Context dataset for detecting only the person class were explored. It was found that, in this person class detection problem, Mild-NMS consistently outperformed both NMS and Soft-NMS in the subsets with the highest degree of intra-class overlap for all four datasets tested. In particular, it performed best on the three highest overlap subsets for the first dataset, the two highest overlap subsets for the second dataset, and the highest overlap subsets in the last two datasets. When considering only the subsets with the highest rate of overlap for each dataset, Mild-NMS outperformed the next best-performing suppression algorithm by an average of 0.02773 in terms of Average Precision. This was done while incurring no additional computational cost, making this suppression algorithm particularly advantageous for object detection tasks where consistently high overlap between objects is expected. © 2024 IEEE.

关键词： Object recognition

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A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

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computer Modeling in Engineering & Sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China The Center for Intelligent Systems and Applications Research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency ***,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this ***,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

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A Federated Stochastic Multi-Level Compositional Minimax Algorithm for Deep AUC Maximization 41

A Federated Stochastic Multi-Level Compositional Minimax Alg...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Xinwen Payani, Ali Lee, Myungjin Souvenir, Richard Gao, Hongchang Department of Computer and Information Sciences Temple University Philadelphia United States Cisco Systems Inc. United States

AUC maximization is an effective approach to address the imbalanced data classification problem in federated *** the past few years, a couple of federated AUC maximization approaches have been developed based on the minimax ***, directly solving a minimax optimization problem to maximize the AUC score cannot achieve satisfactory *** address this issue, we propose to maximize AUC via optimizing a federated multi-level compositional minimax ***, we develop a novel federated multi-level compositional minimax algorithm with rigorous theoretical guarantees to solve this new learning paradigm in both algorithmic design and theoretical *** the best of our knowledge, this is the first work studying the multi-level minimax optimization ***, extensive empirical evaluations confirm the efficacy of our proposed approach. Copyright 2024 by the author(s)

关键词： Federated learning

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