Traditional Detection Methods Research Articles

Design and Research of a Certain Type of Automatic Alarm Equipment

Rapid detection of oxygen leaks in pipelines is crucial to prevent catastrophic accidents. This paper presents the design of an oxygen leakage detection and alarm instrument that converts ultrasonic signals, generated by gas escaping through small holes, into audible frequencies for headset use. This instrument is particularly suitable for ensuring the airtightness of oxygen supply systems for flight crews, engine startup supplemental oxygen systems, and four-station oxygen supply equipment. The design integrates ultrasonic principles, signal processing, computer control, and anti-jamming technologies to detect even minor leaks from a distance. The modular design includes components such as ultrasonic sensors, preamplifiers, filtering circuits, and microcontrollers, ensuring high detection sensitivity and reliability. The alarm system features light and sound alerts, enhancing maintenance efficiency and safety. This modern precision testing instrument significantly improves upon traditional leak detection methods like soapy water tests.

YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Production defects caused by irresistible factors such as process design problems or differences in steel properties in strip production affect the economic benefits of the enterprise and threaten production safety. Traditional defect detection methods are difficult to achieve real-time and high-precision detection, so developing surface defect detection methods based on deep learning is of great significance for strip production. In order to effectively improve the accuracy of the deep learning model in detecting surface defects on hot-rolled strip, in this work we propose a real-time detection model for surface defects on strip steel based on the YOLOv8n model. Firstly, the newly convolutional layer Con5v is designed to replace the original convolutional layer in the neck, and an attention mechanism is added in front of each Con5v to improve the algorithm’s ability to extract small target information. Secondly, an additional set of upsampled feature extraction units is added to the neck in order to enhance the spatial information of the feature map. Subsequently, a set of feature fusion units is incorporated and the convolutional layers in it are improved to provide better feature maps. Thirdly, the number of decoupling detection heads is increased to receive more high-quality features. The final experimental results show that YOLOv8n-GAM (YOLOv8 Nano Model with Global Attention Mechanism) achieves 81.4mAP and 82.0FPS on the NEU-DET dataset and 71.2mAP and 55.0FPS on the GC10-DET dataset, which are 5.7% and 6.9% higher than those of YOLOv8n, respectively. The model proposed in this paper achieves a comprehensive performance improvement in strip steel.

Building point cloud reconstruction in TomoSAR based on deep learning semantic segmentation

AbstractTomographic synthetic aperture radar (TomoSAR) possesses 3D imaging capability, making it significant for building reconstruction using TomoSAR data. The reconstruction algorithm is closely related to building point cloud detection, while traditional detection methods suffer from low automation and reliance on manual configuration. This study proposes a building point cloud reconstruction method based on deep learning semantic segmentation. Initially, deep learning method is employed for end‐to‐end building point cloud segmentation, followed by point cloud reconstruction based on the segmentation results. The proposed method is simple and efficient, elevating the level of automation in point cloud processing. Experimental validation on real TomoSAR data confirms that the proposed method achieves automated and refined reconstruction of building point clouds.

Multivariate impedance spectroscopy method for the measuring of quality and status of electric power oils

With the rapid development of power technology and the complexity of power system equipment, efficient and accurate assessment of the quality and condition of electric power equipment oil (EPEO) has become particularly critical. EPEO is an important factor to ensure the stable operation of power equipment, and its quality and state directly affect the safety and reliability of equipment. However, there are many challenges with traditional oil measuring techniques, which often rely on destructive testing, which not only increases maintenance costs, but can also cause damage to the equipment itself. In the face of these limitations, there is an urgent need to study new oil detection technologies and methods to meet the high standards of modern power systems for high efficiency, non-destructive and comprehensive analytical capabilities. In this paper, a new EPEO measuring technique based on multivariable impedance spectroscopy (MIS) is proposed. Through in-depth analysis of oil's impedance response characteristics under electric field excitation with different frequency., a new approach is provided for the comprehensive evaluation of oil's performance. MIS technology not only has the characteristics of non-destructive testing, ensuring the non-destructive measuring of EPEO, but also its rapid response and real-time analysis ability significantly improves the monitoring efficiency. Based on the proposed MIS detection method, a detection system and experimental prototype which can detect and evaluate the performance and quality of power oil more accurately are designed. Compared with the traditional measuring device, the measuring device utilized in this method can employ three variables. Specifically, it covers a frequency range for the detectable excitation signal spanning from 1 to 100 kHz, an amplitude range from 0.1 to 11.7 V, and a temperature range from −100 °C to 100 °C. The MIS detection method has the capability to identify a variety of parameters, including the dielectric constant, volume resistivity, and dielectric loss factor, among others. This method encompasses a broader spectrum of parameters compared to traditional detection methods, which typically focus on one or two detectable indicators. The correctness and feasibility of the proposed multivariable impedance spectrum detection technique are verified, which provides a new way for the comprehensive evaluation of oil's performance.

Illuminate: Depression diagnosis, explanation and proactive therapy using prompt engineering

Traditional methods of depression detection on social media forums can classify whether a user is depressed, but they often lack the capacity for human-like explanations and interactions. This paper proposes a next-generation paradigm for depression detection and treatment strategies. It employs three Large Language Models (LLMs) - Generative Pre-trained Transformer 4, Llama, and Gemini, each fine-tuned using specially engineered prompts to effectively diagnose, explain, and suggest therapeutic interventions for depression. These prompts are designed to guide the models in analyzing textual data from clinical interviews and online forums, ensuring nuanced and context-aware responses. The study utilizes a few-shot prompting methodology for the Diagnosis and Explanation component. This technique is optimized to provide DSM-5 based analysis and explanation, enhancing the model’s ability to identify and articulate depressive symptoms accurately. Models engage in empathetic dialogue management, guided by resources from Psychology Database and a Cognitive Behavioral Therapy guide, and fine-tuned using Chain of Thought and Tree of Thought prompting techniques. This facilitates meaningful interactions with individuals facing major depressive disorders, fostering a supportive and understanding environment. The research innovates in case conceptualization, treatment planning and therapeutic interventions by creating the Illuminate Database to guide the models in offering personalized therapy. The quantitative analysis of the study is demonstrated through metrics such as F1 scores, Precision, Recall, Cosine similarity, and ROUGE score across different test sets. This comprehensive approach offered through a mobile application prototype, with established psychological methodologies showcases the potential of LLMs in revolutionizing diagnosis and treatment strategies.

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Abstract: Security concerns in internet communication, particularly phishing attacks, pose significant challenges to safeguarding user information. Phishing attacks aim to steal personal data by mimicking legitimate websites, making traditional detection methods less effective. Machine learning-based approaches, though widely used, often struggle with concealed phishing websites and evolving tactics by attackers. Key advancements include the integration of more layers and larger training datasets, along with feature extraction from the Phish Tank dataset. Our proposed model comprises seven layers, culminating in a specialized output layer structure. Experiment results showcase the efficacy of our approach, underscoring its potential to significantly enhance internet security. Furthermore, our model's ability to adapt to highly concealed phishing websites and the dynamic nature of attackers' tactics marks a significant improvement over existing methodologies. By incorporating a comprehensive feature set and leveraging deep learning techniques, our approach achieves state-of-the-art performance in phishing website detection.

A ratiometric and selective fluorescent nanosensor for determining Hg2+ ions based on a novel functionalized metal–organic framework

A new method using fluorescence was developed to detect Hg2+ ions in water. This method relied on the reaction between NH2‐MIL‐53(Al) and 5‐bromo‐2‐hydroxyacetophenone. The method was carefully fine‐tuned for optimal reaction conditions and analytical parameters like pH levels, nanosensor quantity, and detection limits. It showed great linearity in detecting Hg2+ ions in the range of 0.0–3.0 ppm (0.0–1.50 × 10−5 M), with a very low detection limit of 1.65 × 10−2 ppm (8.23 × 10−8 M). We tested how well the method can distinguish between different substances by investigating whether other common substances in water samples would interfere. The results were positive, as the presence of these substances did not significantly affect the accuracy of detecting Hg2+ ions, confirming the method's reliability. In order to confirm its dependability, we conducted a quantitative test to detect Hg2+ ions in water samples. The recovery rates obtained ranged from 97.16% to 103.2%, demonstrating the method's precision and accuracy. A comparison with inductively coupled plasma optical emission spectrometry (ICP‐OES), a well‐known method for analyzing trace metals, produced similar results, further instilling confidence in the 5Br2HA = N‐Al‐MOF nanosensor that was developed. In general, this research emphasizes the exciting potential of the new nanosensor in detecting Hg2+ ions in various water and cosmetic samples. It provides a strong and dependable alternative to traditional detection methods.

Leveraging Cryptographic Hash Functions for Credit Card Fraud Detection

Credit card fraud remains a significant challenge in the financial industry, posing substantial financial losses to both consumers and businesses. Traditional fraud detection methods often rely on rule-based approaches and statistical models, which may struggle to keep pace with evolving fraud tactics and sophisticated cyber threats. In this paper, we propose a novel approach to credit card fraud detection leveraging cryptographic hash functions. Cryptographic hash functions offer robust security guarantees, including collision resistance and preimage resistance, making them well-suited for ensuring the integrity and authenticity of transaction data. Our proposed system employs cryptographic hash functions, such as SHA-256, to generate unique hash values for credit card transactions. These hash values serve as digital fingerprints of the transaction data, enabling secure verification and auditing of transactions on the blockchain. We conducted experiments using a dataset of 100,000 credit card transactions, evaluating the performance of our system in terms of accuracy, precision, recall, and F1-score. The results demonstrate the effectiveness of our approach in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we discuss the implications of our findings and explore future research directions, including the integration of advanced cryptographic techniques and blockchain technology to enhance the security and privacy of credit card transactions. Overall, our study underscores the importance of cryptographic hash functions in building robust and secure fraud detection systems capable of combating emerging fraud threats in the digital era.

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide information on the vertical distribution of the subsurface phytoplankton. In contrast, active LiDAR technology can provide detailed profiles of the subsurface phytoplankton layer (SPL). Nevertheless, the large amount of data generated by LiDAR brought a challenge, as traditional methods for SPL detection often require manual inspection. In this study, we investigated the application of supervised machine learning algorithms for the automatic recognition of SPL, with the aim of reducing the workload of manual detection. We evaluated five machine learning models—support vector machine (SVM), linear discriminant analysis (LDA), a neural network, decision trees, and RUSBoost—and measured their performance using metrics such as precision, recall, and F3 score. The study results suggest that RUSBoost outperforms the other algorithms, consistently achieving the highest F3 score in most of the test cases, with the neural network coming in second. To improve accuracy, RUSBoost is preferred, while the neural network is more advantageous due to its faster processing time. Additionally, we explored the spatial patterns and diurnal fluctuations of SPL captured by LiDAR. This study revealed a more pronounced presence of SPL at night during this experiment, thereby demonstrating the efficacy of LiDAR technology in the monitoring of the daily dynamics of subsurface phytoplankton layers.

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences. Our objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children's National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors. Our study design optimized eight ML algorithms - Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network - to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score. The results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model's performance is comparable to other common ML models. Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

Enhancing Safety and Workover Efficiency Through Real-Time Scale-Adaptive Video Detection of Critical Objects at the Wellsite

Summary The wellsite serves as the fundamental unit in the development of oil and gas fields, functioning as a hub for the production activities, with workover operations being a critical means to ensure production continuity. In addition, it plays a crucial role in environmental protection, preventing oil and gas leakage and pollution. Various pieces of mechanical equipment deployed at the wellsite are essential for tasks such as oil and gas extraction and well repair operations, holding a pivotal position in oil- and gasfield development. Consequently, intelligent wellsite implementation necessitates a primary focus on monitoring mechanical equipment, with video emerging as a vital form of multisource information at the wellsite. While existing research on wellsite video monitoring predominantly addresses system and data transmission issues, it falls short in addressing the challenges of real-time assessment and early warning in intelligent wellsite operations. This study introduces a method for identifying critical targets at the wellsite based on a scale-adaptive network. The model employs a multiscale fusion network to extract different image features and semantic features at various scales, facilitating their fusion. The processing of wellsite video images occurs in multiple stages, outputting predicted box locations and category information, enabling the localization and recognition of critical objects at the wellsite. Unlike traditional deep convolutional object detection methods, this model incorporates a parameter-free attention mechanism, enhancing the accurate feature learning of small targets during the extraction process and addressing the issue of multiscale imbalance. The experimental results validate the robust performance of the method, surpassing the latest one-stage object detection models and mainstream loss function methods. Comparative experiments demonstrate a 9.22% improvement in mean average precision (mAP) compared with YOLOv8, establishing the proposed model as a top performer in loss function optimization experiments. Furthermore, we propose a video security detection model whose results, combined with the recognition model, are applicable for video detection in wellsite scenarios. The model exhibits strong integration capabilities for scene area division and behavior regulation monitoring. In addition, the model provides valuable insights for analyzing equipment operating status, aligning with the practical needs of oil fields.

Long-short term memory networks aided fault detection of power facilities

In recent years, long-short term memory (LSTM) networks have emerged as a promising tool for time-series analysis, offering the ability to capture temporal dependencies effectively, especially for the fault detection in power facilities which is crucial for maintaining operational integrity and ensuring continuous power supply. This paper presents a study on the utilization of LSTM networks for fault detection in power facilities, focusing on evaluating the detection accuracy as a performance metric. We propose a novel LSTM-based fault detection framework that utilizes historical operational data to identify abnormal patterns indicative of faults or anomalies. The framework is trained on labeled data to learn the normal operational behavior of the power facility, enabling it to detect deviations from expected patterns in practical data streams. To assess the effectiveness of the proposed approach, extensive experiments are conducted using real-world operational data from diverse power facilities. We systematically evaluate the detection accuracy of the LSTM-based fault detection system under various operating conditions, including different types of faults and levels of noise in the data. Comparative analyses are performed against traditional fault detection methods to demonstrate the superiority of the LSTM-based approach in terms of accuracy. Moreover, sensitivity analyses are conducted to investigate the impact of key parameters, such as network architecture and training data size, on the detection performance. The results illustrate the robustness and effectiveness of the proposed LSTM-based fault detection framework, highlighting its potential for enhancing the reliability and efficiency of power facility operations.

Optimization of metal–organic framework nanozyme activity via histidine modification for simultaneous pesticide detection

Biosensors provide simple, rapid and sensitive detection of a wide range of analytes. Recently, they have been widely used for the rapid detection of pesticides, which makes up for the shortcomings of traditional detection methods. However, the catalytic activity of some nanozymes is not satisfactory in practical applications, limiting the performance of biosensors. Further development of highly active and stable nanozymes is conducive to improving the sensitivity of the sensors and expanding their application in practical detection. Herein we simulated the amino acid microenvironment of natural enzymes and synthesized His-MIL-101(Fe)-X with different histidine (His) modification amounts (X = 0, 25%, 50%, 75%) by regulating the ratio of histidine and terephthalic acid ligands using a one-step solvothermal method. With increasing histidine doping, the peroxidase-like activity of His-MIL-101(Fe)-X was gradually enhanced, and the sensing performance of the sensor arrays constructed by combining His-MIL-101(Fe)-X with three peroxidase substrates was also gradually improved, with a 25-fold increase in detection limit. The His-MIL-101(Fe)-75% sensor array was not only able to accurately discriminate five pesticides in the range of 2-100 μM, but also to quantitatively identify different concentrations of pesticides. Moreover, the His-MIL-101(Fe)-75% sensor array showed good anti-interference capability and was able to discriminate five pesticides at only 2 μM in soil, lake water, seawater, and apple samples, as well as quantitatively detecting diafenthiuron, which has great potential for practical application.

Low-Cost Portable Road Smoothness Testing Method Based on Pseudo-Vibration Velocity Range

Road smoothness not only directly affects the safety and comfort of vehicle travel but also relates to the efficiency and cost-effectiveness of road maintenance. Traditional road smoothness detection methods usually require professional equipment and personnel, leading to high costs and cumbersome operations. Therefore, finding a low-cost, simple, and accurate method for detecting road smoothness is of great significance. This study uses vehicle-mounted acceleration sensors to detect road smoothness, establishing a correlation between driving vibration acceleration data and the international roughness index (IRI). For this research, a driving vibration acceleration data acquisition device was developed, and the driving acceleration data from the test sections were denoised and their feature values extracted. The pseudo-vibration velocity range was used as the characteristic index representing the road surface smoothness IRI value. Testing with different vehicle types showed that the method is applicable to both sedans and SUV models, yielding a relative error of 8.9% for the sedan smoothness test model and 6.7% for the SUV smoothness test model. This study contributes to conducting large-scale road smoothness detection at a low cost, improving the efficiency of road maintenance and operations.

Rapid and real-time analysis of multi-component dissolved gas in seawater by Raman spectroscopy combined with continuous gas−liquid separator

Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle. Large quantities of in situ optical detection techniques showed restricted measurement efficiency, owing to the single gas sensor without the identification ability of multiple gases. In this work, a novel gas−liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas−liquid separator under a large difference of partial pressure. The limit of detection (LOD) of the gas Raman spectrometer could arrive at about 14 μl·L−1 for N2 gas. Moreover, based on the continuous gas−liquid separation process, the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method (30 min). Effect of equilibrium time on gas−liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO2 >O2 >N2. In addition, the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed. The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.

Construction of a pH- and viscosity-switchable near-infrared fluorescent probe and its imaging application

Viscosity is a parameter used to measure the fluidity of liquids and a key indicator in evaluating the states of body fluid in biological tissues and lesions. Most traditional detection methods have many drawbacks such as a short emission wavelength and interference by background fluorescence. Inspired by the multiple double bond structure of retinal, a novel pH and viscosity dual-response fluorescent probe (Rh-TR) was constructed in this study. Rh-TR exhibited two emission signals centered at 510 and 660 nm. As the pH of the phosphate-buffered saline increased, the fluorescence at 510 nm increased by about 124-fold, while the change in fluorescence at 660 nm was not obvious. When detecting the change in viscosity using the probe, the fluorescence at 510 nm decreased by about 85 %, while the fluorescence at 660 nm increased by over 20-fold. The probe also showed high selectivity and little toxicity. As demonstrated by the biological imaging experiment, the probe successfully imaged changes in the pH and viscosity of cells and in a live animal model of zebrafish. Considering the unique structure of Rh-TR with retinal and its pH- and viscosity-switchable spectral property, the probe may find further application in detecting viscosity-related diseases and industrial detection.

Detecting contract cheating through linguistic fingerprint

Contract cheating, the act of students enlisting others to complete academic assignments on their behalf, poses a significant challenge in academic settings, undermining the integrity of education and assessment. It involves submitting work that is falsely represented as the student’s own, thus violating academic standards and ethics. The advent of artificial intelligence-based language models, such as ChatGPT, has raised concerns about the potential impact of contract cheating. As these language models can generate human-like text with ease, there are concerns about their role in facilitating and increasing contract cheating incidents. Innovative approaches are thus needed to detect contract cheating and address its implications for academic integrity. This study introduces a machine learning (ML) model focused on identifying deviations from a learner’s unique writing style (or their linguistic fingerprint) to detect contract cheating, complementing traditional plagiarism detection methods. The study involved 150 learners majoring in engineering and business who were studying English as a foreign language at a college in Saudi Arabia. The participants were asked to produce descriptive essays in English within a consistent genre over one semester. The proposed approach involved data preprocessing, followed by transformation using Term Frequency-Inverse Document Frequency (TF-IDF). To address data imbalance, random oversampling was applied, and logistic regression (LR) was trained with optimal hyperparameters obtained through grid search. Performance evaluation was conducted using various metrics. The results showed that the ML model was effective in identifying non-consistent essays with improved accuracy after implementing random oversampling. The LR model achieved an accuracy of 98.03%, precision of 98.52%, recall of 98.03%, and F1-score of 98.24%. The proposed ML model shows promise as an indicator of contract cheating incidents, providing an additional tool for educators and institutions to uphold academic integrity. However, it is essential to interpret the model results cautiously, as they do not constitute unequivocal evidence of cheating but rather serve as grounds for further investigation. We also emphasize the ethical implications of such approaches and suggest avenues for future research to explore the model’s applicability among first-language writers and to conduct longitudinal studies on second-language learners’ language development over longer periods.

Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet

Road crack detection is crucial for maintaining and inspecting civil infrastructure, as cracks can pose a potential risk for sustainable road safety. Traditional methods for pavement crack detection are labour-intensive and time-consuming. In recent years, computer vision approaches have shown encouraging results in automating crack localization. However, the classical convolutional neural network (CNN)-based approach lacks global attention to the spatial features. To improve the crack localization in the road, we designed a vision transformer (ViT) and convolutional neural networks (CNNs)-based encoder and decoder. In addition, a gated-attention module in the decoder is designed to focus on the upsampling process. Furthermore, we proposed a hybrid loss function using binary cross-entropy and Dice loss to evaluate the model’s effectiveness. Our method achieved a recall, F1-score, and IoU of 98.54%, 98.07%, and 98.72% and 98.27%, 98.69%, and 98.76% on the Crack500 and Crack datasets, respectively. Meanwhile, on the proposed dataset, these figures were 96.89%, 97.20%, and 97.36%.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.