Efficient Identification Research Articles

Fish Species Detection Using Deep Learning

Abstract: This research explores the application of deep learning techniques for fish species detection in underwater environments. convolutional neural networks (CNNs) trained on extensive datasets, the study aims to enhance the accuracy and efficiency of species identification. The proposed model demon- strates promising results in differentiating diverse fish species, contributing to advancements in aquatic ecology monitoring and biodiversity conservation. The integration of deep learning in fish species detection holds potential for improving our understanding of underwater ecosystems and supporting sustainable fisheries management. The relative abundance of fish pieces in their habitats on a regular basis and keeping an eye on population fluctuations, this are a crucial task for marine scientists andconservationists diverse automatic computer based fish sample methods have been demonstrated in underwater photos and videos as alternatives to time consuming hand sampling there isn’t however a perfect method for automatically detecting fish and classifying the species this is mostly due to the difficulties in producing clear underwater images and videos which include environmental fluctuations in lightning fish camouflage Dynamicbackdrops murky water low resolution shape deformations of moving fish.

Enhancing chromosomal analysis efficiency through deep learning-based artificial intelligence graphic analysis

The objective of this study is to evaluate the efficacy and diagnostic utility of an advanced chromosomal analysis approach. A total of 2663 amniotic fluid samples were chosen for chromosomal karyotype profiling between January 2022 and June 2023. Two sets of tests were carried out: experiment 1 involved randomly selecting 1168 examples to test the accuracy of machine learning-based chromosomal karyotypes. The aim was to determine the method’s general applicability when cases were naturally dispersed. Experiment 2 concentrated on randomly selecting the most common examples of chromosomal number anomalies and cases with structural defects that did not affect the visual assessment of chromosome categories. The goal was to investigate the diagnostic efficacy of the artificial intelligence (AI) analysis system in detecting these flaws. The results of experiment 1 demonstrated the resilience of the intelligent analysis system in cases with significant differences in chromosomal karyotypes, resulting from manual shooting and film-making. Experiment 2 results showed that the intelligent analysis system surpassed the standard chromosomal image analysis program in terms of automated analysis accuracy, for both normal and defect cases. Furthermore, the intelligent analysis system demonstrated detection and analysis speeds that were 3–15 times faster. The average speed of regular case analysis increased by a factor of 4–6, cases with quantitative defects increased by a factor of 3–5, and cases with structural defects increased by a factor of 5–7. Implementing a chromosome intelligence analysis system in clinical practice could improve the efficiency of chromosome identification and analysis, allow for more widespread chromosomal examination, and reduce the likelihood of congenital defects.

DETECTION OF ANDROID BOTNETS

In the dynamic landscape of cybersecurity, the persistent proliferation of botnets remains a formidable challenge. Conventional detection methods often grapple with elevated false positive rates and struggle to keep pace with the evolving tactics employed by malicious actors. This study delves into the domain of botnet detection, with a primary focus on refining the identification process for HTTP-based botnets. Our approach harnesses the power of K-Nearest Neighbors (KNN) and Logistic Regression algorithms, strategically amalgamating their capabilities to navigate the intricate digital terrain. Departing from prior studies, we directly confront the botnet detection challenge by synergizing two robust machine learning techniques. Through the fusion of KNN and Logistic Regression, our system achieves unparalleled accuracy and efficiency in discerning HTTP botnet activities. Furthermore, this research pioneers a groundbreaking methodology for botnet detection, centering around the innovative fusion of TFIDF and Textrank algorithms. This hybrid approach significantly bolsters the precision of information extraction and summarization. In an age inundated with vast digital datasets, our method adeptly sifts through information while furnishing concise and relevant summaries, thereby conserving invaluable time and resources. The distinctive aspect of our approach lies in its ability to swiftly adapt to emerging botnet strategies. Through extensive testing and comparative analysis against existing models, our system surpasses prior methodologies, demonstrating its proficiency in accurately identifying botnet activities while minimizing false positives. Furthermore, our solution serves as an exemplar of efficiency, facilitating prompt and effective identification of pernicious botnets that imperil data security Key Words: Botnet Detection, Cybersecurity, K-Nearest Neighbors (KNN), Logistic Regression, TFIDF, Textrank, Information Summarization, Digital Security, Malware Detection.

P.069 Development of a streamlined multi-disciplinary care pathway for compression neuropathies

Background: Care for patients with compression neuropathies (carpal tunnel syndrome, ulnar neuropathy) is often fragmented, uncoordinated, and slow. Patients go through multiple steps (neurology consultation, nerve testing, ultrasound, splints, injection, surgical opinion, surgery) with waits between each step. We used a Value-Based Health Care (VBHC) model to develop a multidisciplinary clinic with a novel care pathway. Methods: A Shared Care initiative supported the development of an Integrated Practice Unit (IPU). Key multidisciplinary team members were identified. Participants attended a curated three part VBHC workshop. Process mapping enabled identification of efficiencies. Results: 14 team members participated in the workshops. Condition specific outcome measures were identified (Boston CTS measure, 10-point touch, MRC strength and pain scale) and will be collected longitudinally. Criteria and clinical pathways were developed for mild, moderate, and severe carpal tunnel syndrome. Resource materials for patients and providers were developed. Conclusions: A VBHC framework supported development of a novel clinic for compression neuropathy. Responsibility for the full cycle of care rests with the IPU. Systematically tracking functional outcome measures enables quality improvement. By streamlining the patient journey and substantially reducing wait times between steps, the new care pathway reduces complexity and improve outcomes. Evaluation of impact if this new clinical model is ongoing.

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism.

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction-attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

Amphiphilic AIE Fluorescent Probe: A Dual-Functionality Strategy for Efficient Antibacterial Therapy Fluorescence Bioimaging against Staphylococcus aureus.

Drug-resistant bacteria present a grave threat to human health. Fluorescence imaging-guided photodynamic antibacterial therapy holds enormous potential as an innovative treatment in antibacterial therapy. However, the development of a fluorescent material with good water solubility, large Stokes shift, bacterial identification, and high photodynamic antibacterial efficiency remains challenging. In this study, we successfully synthesized an amphiphilic aggregation-induced emission (AIE) fluorescent probe referred to as NPTPA-QM. This probe possesses the ability to perform live-bacteria fluorescence imaging while also exhibiting antibacterial activity, specifically against Staphylococcus aureus (S. aureus). We demonstrate that NPTPA-QM can eliminate S. aureus at a very low concentration (2 μmol L-1). Moreover, it can effectively promote skin wound healing. Meanwhile, this NPTPA-QM exhibits an excellent imaging ability by simple mixing with S. aureus. In summary, this research presents a straightforward and highly effective method for creating "amphiphilic" AIE fluorescent probes with antibacterial properties. Additionally, it offers a rapid approach for imaging bacteria utilizing red emission.

A Multimodal Fusion Behaviors Estimation Method for Public Dangerous Monitoring

At the present stage, the identification of dangerous behaviors in public places mostly relies on manual work, which is subjective and has low identification efficiency. This paper proposes an automatic identification method for dangerous behaviors in public places, which analyzes group behavior and speech emotion through deep learning network and then performs multimodal information fusion. Based on the fusion results, people can judge the emotional atmosphere of the crowd, make early warning, and alarm for possible dangerous behaviors. Experiments show that the algorithm adopted in this paper can accurately identify dangerous behaviors and has great application value.

Research on and Application of Feature Recognition and Intelligent Retrieval Method for Multi-Component Alloy Powder Injection Molding Gear Based on Partition Templates

The forming process of multi-alloy gears by metal powder injection molding is tedious, and the current design process mainly depends on the experience of designers, which seriously affects the product development cycle and forming quality. In order to solve the problem of the gear feature expression being missing, which hinders the automatic retrieval of similar parts in the analogical design process, a feature recognition and intelligent retrieval method for a multi-alloy powder injection molding gear based on partition templates is proposed in this paper. The partition templates of the gear are defined, and gear digitization is completed by using the automatic recognition algorithm. Searching for similar gear parts in the knowledge base, designers can analogically design the forming process for new parts according to the mature process of the parts in the knowledge base. The automatic identification and intelligent retrieval system developed according to this method has been implemented in two MIM (metal injection molding) product manufacturing enterprises. Case studies and industrial applications have proved the effectiveness of the system, the efficiency of identification and retrieval has been improved by more than 97%, and the number of mold tests has been reduced by 60%.

Development and validation of fractional constitutive models for viscoelastic-plastic creep in time-dependent materials: Rapid experimental data fitting

This study aims to address the core challenges in the field of fractional models: developing fractional viscoelastic models with clear physical meanings and efficient, precise methods for solving fractional differential equations. Through the analysis of experimental data, this study proposes several concise and accurate models to predict creep phenomena, characterized by a minimal number of parameters, each with a clear physical interpretation. These models demonstrate high accuracy in describing the nonlinear behavior during the accelerated creep stage and successfully overcome the difficulty of the fractional order exceeding 1. Additionally, the research introduces an improved memoryless technique for the numerical solution of fractional viscoelastic models, enhancing the efficiency of parameter identification. The methodology of this study is practical and feasible, offering valuable references for understanding and predicting the creep behavior of viscoelastic materials.

Steam generator efficiency optimization under uncertainty through multi-fidelity modeling

The steam generator is an essential equipment in power plants, and understanding its behavior with the aid of mathematical models allows for improved decision-making aiming toward better efficiency. Well-calibrated models can help identify new operating conditions that lead to efficiency enhancement. Unfortunately, due to the complexity of steam generators, computationally efficient models based solely on energy-mass balance usually present limiting simplifications. Alternatively, computational fluid dynamics can solve reacting flow phenomena but require a great amount of computational time. Purely data-driven models often extrapolate poorly as they do not necessarily contain the physical equations that drive the system’s behavior. In this scenario, the multi-fidelity approach is an attractive solution because it combines physics-grounded representation with a low computational footprint and allows for mapping the efficiency of the steam generator considering uncertainty propagation. This paper presents a multi-fidelity model that simulates the efficiency of a steam generator from the PECÉM power plant. The model is assembled by the sum of two Gaussian processes, trained using one high-fidelity and one low-fidelity dataset. The high-fidelity dataset is formed by the observed operation of the actual power plant at varying operating conditions. These same operating parameters are the input for the energy-mass balance model built using the EBSILON software to generate the low-fidelity dataset. The observed efficiency of the PECÉM power plant ranged from 82.89% to 84.37%, with a mean value of 83.58%. The resulting multi-fidelity model presents a root mean square error of 0.19%, which is six times lower than the error obtained by the energy-mass balance model. This model is then used for global sensitivity analysis and robust optimization. A Pareto front mapping expected efficiency and its confidence level is built by exploring not yet experimented operational points. Finally, data clustering allowed the identification of efficiencies around 86%, which adds 1.63% to the power plant’s maximum level.

Diagnosis of Medical Images Using Convolutional Neural Networks

Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.

Critical component detection in assemblies: a graph centrality approach

AbstractThis study examines the use of graph centrality to identify critical components in assembly models, a method typically dominated by computationally intense analyses. By applying centrality measures to simulated assembly graphs, components were ranked to assess their criticality. These rankings were compared against Monte Carlo sensitivity analysis results. Preliminary findings indicate a promising correlation, suggesting graph centrality as a valuable tool in assembly analysis, enhancing efficiency and insight in critical component identification.

A new hydrogen-filled Cherenkov detector for Kaon tagging at the NA62 experiment at CERN

The precision measurement of K + → π + νν̅ at the NA62 experiment requires a kaon identification detector to have a time resolution better than 100 ps, at least 95 % kaon identification efficiency, and a pion misidentification probability of less than 10-4. Since the start of NA62 data taking in 2016, kaon identification has been performed by a differential Cherenkov with achromatic ring focus (CEDAR) detector with a nitrogen gas radiator. A new CEDAR using hydrogen (CEDAR-H) as a radiator gas has been developed to reduce the material in the beamline, reducing the beam particle scattering within the detector.CEDAR-H was validated during a two-week test beam at CERN in 2022 and was approved by the NA62 collaboration for use in data taking from 2023. The test beam results, installation and commissioning in the NA62 beamline are reported.

Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data

Abstract. The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. In response, our study aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. This novel algorithm incorporates an optical database, constructed using the Mie scattering model, and employs a random forest algorithm to classify different aerosol types based on the optical data from the database. The complex refractive index was used as a baseline to assess the performance of our hybrid algorithm against the traditional Gaussian kernel density clustering method for aerosol-type identification. The hybrid algorithm demonstrated impressive consistency rates of 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse (mixed, course-mode aerosol), mixed-fine (mixed, fine-mode aerosol), urban/industrial, and biomass burning aerosols, respectively. Moreover, it achieved remarkable precision, with evaluation metric indexes for micro-precision, micro-recall, micro-F1-score, and accuracy of 95 %, 89 %, 91 %, and 89 %, respectively. Lastly, a global map of aerosol types was generated using the new hybrid algorithm to characterize aerosol types across the five continents. This study, utilizing a novel approach for the classification of aerosol, will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.

Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB)

This study presents a generalized hybrid model for predicting H2S and VOCs removal efficiency using a machine learning model: K–NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94–0.99, SENS = 0.96–0.99). It was shown that the hybrid K-NN model applied for the “Klimzowiec” WWTP, including the pilot plant, can also be applied to the “Urbanowice” WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.

Data mining algorithm in the identification of accounting fraud by smart city information technology

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

A non-targeted analysis workflow for the identification of organic contaminants in a sludge water based on fragmentation matching score and metadata

ABSTRACT Non-targeted analysis (NTA) methods have been increasingly used to discover organic contaminants in environmental samples. The identification efficiency of conventional NTA workflow is often very low due to many factors such as low fragmentation score between experimental MS2 data and reference MS2 data/in-silico MS2 data. Additionally, the prioritisation of possible candidates is also challenging due to the large number of candidates obtained from MS acquisitions. In this study, a multiplatform data processing workflow applicable to non-targeted LC-MS data is proposed to improve the identification accuracy of organic contaminants in water. This workflow consists of (1) scanning quadrupole data-independent acquisition (SONAR) to acquire relatively clean MS1 and MS2 data, (2) data deconvolution using Progenesis QI Software to filter/select the appropriate ions (MS1 data), (3) the selected MS1 data matching against U.S. EPA Chemistry Dashboard to get a list of the environmentally related candidates with metadata (e.g., data sources, toxicity, and exposure), (4) experimental MS2 data matching against in-silico fragmentation data of the obtained candidate by MetFrag to obtain a fragmentation score, (5) compound identification through ranking the candidates by MetFrag based on fragmentation score and metadata. The identification efficiency of the metadata-based NTA workflow was evaluated using a 33-component mixture of chemicals in a complex sludge water. Out of 33 spiked compounds, 26 compounds had been identified using the defined NTA workflow. Most of the 26 identified compounds were recognized as 1st hit of the candidates obtained from the library searching. Overall, the incorporation of fragmentation score and metadata to rank the candidates has significantly improved the accuracy of the organic contaminant identification in the sludge water, compared to the conventional NTA workflow based solely on the fragmentation score.

Dhanvantri - Automated Plant Detection and Analysis in Medicine using Image Processing and Machine Learning

In recent years there has been an increasing interest in using technology to help identify and classify medicinal plants and their raw materials. In this research paper, we will use image processing techniques and machine learning algorithms to automatically identify various medicinal plants or raw materials. The goal of this approach is to use computer vision to improve the efficiency and precision of plant identification. This approach will help to facilitate botanical research and pharmaceutical manufacturing, as well as traditional medicine practice

Understanding microplastic presence in different wastewater treatment processes: Removal efficiency and source identification

Municipal effluents discharged from wastewater treatment plants (WWTPs) are a considerable source of microplastics in the environment. The dynamic profiles of microplastics in treatment units in WWTPs with different treatment processes remain unclear. This study quantitatively analyzed microplastics in wastewater samples collected from different treatment units in two tertiary treatment plants with distinct processes. The influents contained an average of 15.5 ± 3.5 particles/L and 38.5 ± 2.5 particles/L in the two WWTPs with in the oxidation ditch process and the integrated fixed-film activated sludge process, respectively. Interestingly, microplastic concentrations in the influent were more influenced by the population density in the served area than sewage volume or served population equivalent. Throughout the treatment process, concentrations were reduced to 1.5 ± 0.5 particles/L and 1.0 ± 1.0 particles/L in the final effluents, representing an overall decrease of 90% and 97%, in WWTPs with the oxidation ditch process and integrated fixed-film activated sludge process, respectively. A significant proportion of the microplastics were removed during the primary treatment stage in both WWTPs, with better performance for foam, film, line-shaped and large-sized microplastics. Most microplastics were accumulated in activated sludge, indicating its key role as the primary sink in WWTPs. The multiple correspondence analysis identified laundry washing and daily necessities such as packaging and containers as the major contributors to microplastics in WWTPs. The study proposed recommendations for upgrading WWTPs, modifying designs, and implementing strategies to reduce microplastic sources, aiming to minimize the release of microplastics into the environment. These findings can shed lights on the sources of microplastics in WWTPs, and advance our understanding of the mechanisms for more effective microplastic removals in wastewater treatment technologies in future applications.

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry: An Innovative Tool for Rapid Identification of Hylurgus ligniperda, an Invasive Pest

Hylurgus ligniperda is an imported quarantine plant pest in China. Its identification is usually based on morphological characteristics; therefore, species identification needs high professional requirements of staff and professionals with high experience accumulated through long-term training. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a rapid identification technology, which is based on protein profiles of species. It has been widely used for the identification of pathogenic microorganisms. Many studies have reported the identification of mosquitoes, ticks, and other arthropods. The application of MALDI-TOF MS in the identification of H. ligniperda can improve the identification efficiency of H. ligniperda, preventing and control its harm and further spread. To construct a spectra database for H. ligniperda, we analyzed the effect of different factors, such as different body parts, developmental stages, populations, and preservation conditions, on its protein spectrum. We collected protein spectrum profiles from 19 specimens of H. ligniperda and its related species, obtaining 211 protein spectra to construct a reference database and validate identification. The protein spectrum from the chest specimens of H. ligniperda showed many peaks, high intensity, and a stable signal, indicating a successful data establishment. The difference in protein spectra between different regions of the same species was less, but did not affect the identification results. Clear differences were observed in the protein spectrum across many developmental stages. The database established by the adult specimens protein spectrum can accurately identify Dendroctonus valens, Tomicus piniperda, and H. ligniperda. MALDI-TOF MS technology can be used for the rapid identification of H. ligniperda. This method is rapid and direct, and the identification results are robust. It does not require specialized entomological expertise and can be used for customs interception and field investigations.