Remote Sensing Image Detection Method Combining Dynamic Convolution and Attention Mechanism by Yunfei Zhang, Ming Chen, Cong Chen

Subjects: Get full text

Optimized YOLO Model for Accurate and Real-Time Detection of Machinery Around Shovels in Copper Mining by Mohaddeseh Ghiasi, MasoudReza Aghabozorgi

Get full text

Feature Selection for the Automated Detection of Metaphase Chromosomes: Performance Comparison Using a Receiver Operating Characteristic Method by Yuchen Qiu, Jie Song, Xianglan Lu, Yuhua Li, Bin Zheng, Shibo Li, Hong Liu

“…The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. …”
Get full text

Research on Detection and Optimization Measures of High-speed EMU Power Network Voltage Interruption by Hong SHEN, Dong CHEN, Gang FU

Get full text

Tomato leaf disease detection method based on improved YOLOv8n by Ming Chen, Chunping Wang, Chengwei Liu, Ying Yu, Yuan Yuan, Jiaxuan Ma, Kaisheng Zhang

Subjects: Get full text

Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization by T. Tamilarasi, P. Muthulakshmi, Seyed-Hassan Miraei Ashtiani

“…Unlike conventional object detection models, which require substantial pre-training and curated datasets, the BHDS integrates automated data acquisition and dynamic image quality assessment, enabling effective operation with minimal data input. …”
Get full text

Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning by Akindele S. Afolabi, Olubunmi A. Akinola

“…In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. …”
Get full text

Genetically encoded sensors illuminate in vivo detection for neurotransmission: Development, application, and optimization strategies by Xiaoyu Zhong, Hengyu Gu, Juyao Lim, Peng Zhang, Guangfu Wang, Kun Zhang, Xiaowan Li

Get full text

Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization by Xia Wang, Wei Zhao, Jia-Ning Tang, Zhong-Bin Dai, Ya-Ning Feng

“…Abstract Large-scale sparse multi-objective optimization problems are prevalent in numerous real-world scenarios, such as neural network training, sparse regression, pattern mining and critical node detection, where Pareto optimal solutions exhibit sparse characteristics. …”
Get full text

An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks by Taiwo Blessing Ogunseyi, Gogulakrishan Thiyagarajan

Get full text

Advanced Residual Optimal Mapping Approach for Precise Detection of Stator Faults in Induction Motors by Abderrahim Allal, Zakaria Lammouchi, Abderrahmane Khechekhouche, Naoui Mohamed, Khalid Alqunun, Amer Alghadhban, Ismail Marouani, Tawfik Guesmi

“…However, their susceptibility to failure, particularly in harsh environments, poses significant operational challenges. Early detection of faults is essential to avoid unplanned downtime and costly repairs. …”
Get full text

System for PCB Defect Detection Using Visual Computing and Deep Learning for Production Optimization by Gabriel Gomes de Oliveira, Gabriel Caumo Vaz, Marcos Antonio Andrade, Yuzo Iano, Leandro Ronchini Ximenes, Rangel Arthur

“…Tests were performed using a particular smartphone model that had 22 critical components to inspect and the results showed that the proposed system achieved an average accuracy of more than 90% in defect detection when it was directly used in the operational production line. …”
Get full text

Vision-Based Detection, Localization, and Optimized Path Planning for Rebar Intersections in Automated Construction by Chengxiang Li, Weimin Zhang, Fangxing Li, Meijun Guo, Shicheng Fan

“…To achieve comprehensive site coverage and optimize operational efficiency, the path planning challenge is reformulated as a sequencing optimization problem of the identified intersections. …”
Get full text

AquaYOLO: Advanced YOLO-based fish detection for optimized aquaculture pond monitoring by M. Vijayalakshmi, A. Sasithradevi

“…Our model ensures efficient and affordable fish detection for small-scale aquaculture.…”
Get full text

Optimizing survey conditions for Burmese python detection and removal using community science data by Kelly R. McCaffrey, Melissa A. Miller, Sergio A. Balaguera-Reina, Alexander S. Romer, Michael Kirkland, Amy Peters, Edward F. Metzger, LeRoy Rodgers, Frank J. Mazzotti

“…Abstract Burmese pythons (Python bivittatus) have demonstrated prolific spread and low detectability within their invasive range in Florida, USA. …”
Get full text

Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
Get full text

Optimized PCF architectures for THz detection of aquatic pathogens: Enhancing water quality monitoring. by Diponkar Kundu, Nasir Uddin Badhon, A H M Iftekharul Ferdous, Md Safiul Islam, Md Galib Hasan, Khalid Sifulla Noor, Most Momtahina Bani

“…These improvements improve the sensor's trace bacteria detection. These factors increase the sensor's aquatic germ detection when combined. …”
Get full text

Optimizing the growth conditions of superconducting MoSi thin films for single photon detection by Stefanie Grotowski, Lucio Zugliani, Björn Jonas, Rasmus Flaschmann, Christian Schmid, Stefan Strohauer, Fabian Wietschorke, Niklas Bruckmoser, Manuel Müller, Matthias Althammer, Rudolf Gross, Kai Müller, Jonathan Finley

Get full text

Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification by P. Preethi, R. Swathika, S. Kaliraj, R. Premkumar, J. Yogapriya

“…This study introduces an advanced approach for automated rice plant disease detection and classification by integrating deep learning and metaheuristic optimization techniques. …”
Get full text

Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance by Jirakorn Jamrasnarodom, Pharuj Rajborirug, Pises Pisespongsa, Kitsuchart Pasupa

“…Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. …”
Get full text