Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Application of seismic anisotropy fluid detection technology in the Ken 71 well block of Shengli Oilfield

On the basis of Chapman＇s（2003） model,as the seismic wave incidences angles vary from 0° to 45° while propagating in anisotropic media（HTI）,the slow S-wave will sufferred by serious attenuation and dispersion and is sensitive to fluid viscosity but the P-and fast S-waves don＇t.For slow S waves propagating normal to fractures,the amplitudes are strongly affected by pore fluid.So,the slow S-wave can be used to detect fractured reservoir fluid information when the P-wave response is insensitive to the fluid.In this paper,3D3C seismic data from the Ken 71 area of Shengli Oilfield are processed and analyzed.The travel time and amplitude anomalies of slow S-waves are detected and correlated with well log data.The S-wave splitting in a water-bearing zone is higher than in an oil-bearing zone.Thus,the slow S-wave amplitude change is more significant in water-bearing zones than in oil-bearing zones.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

A core-satellite self-assembled SERS aptasensor containing a“biological-silent region”Raman tag for the accurate and ultrasensitive detection of histamine

Herein,a novel interference-free surface-enhanced Raman spectroscopy(SERS)strategy based on magnetic nanoparticles(MNPs)and aptamer-driven assemblies was proposed for the ultrasensitive detection of histamine.A core-satellite SERS aptasensor was constructed by combining aptamer-decorated Fe_(3)O_(4)@Au MNPs(as the recognize probe for histamine)and complementary DNA-modified silver nanoparticles carrying 4-mercaptobenzonitrile(4-MBN)(Ag@4-MBN@Ag-c-DNA)as the SERS signal probe for the indirect detection of histamine.Under an applied magnetic field in the absence of histamine,the assembly gave an intense Raman signal at“Raman biological-silent”region due to 4-MBN.In the presence of histamine,the Ag@4-MBN@Ag-c-DNA SERS-tag was released from the Fe_(3)O_(4)@Au MNPs,thus decreasing the SERS signal.Under optimal conditions,an ultra-low limit of detection of 0.65×10^(-3)ng/mL and a linear range 10^(-2)-10^5 ng/mL on the SERS aptasensor were obtained.The histamine content in four food samples were analyzed using the SERS aptasensor,with the results consistent with those determined by high performance liquid chromatography.The present work highlights the merits of indirect strategies for the ultrasensitive and highly selective SERS detection of small biological molecules in complex matrices.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

Added value of ratio of cross diameters of the appendix in ultrasound diagnosis of acute appendicitis

BACKGROUND The maximum outer diameter(MOD)of the appendix is an essential parameter for diagnosing acute appendicitis,but there is space for improvement in ultrasound(US)diagnostic performance.AIM To investigate whether combining the ratio of the cross diameters(RATIO)of the appendix with MOD of the appendix can enhance the diagnostic performance of acute appendicitis.METHODS A retrospective study was conducted,and medical records of 233 patients with acute appendicitis and 112 patients with a normal appendix were reviewed.The MOD and RATIO of the appendix were calculated and tested for their diagnostic performance of acute appendicitis,both individually and in combination.RESULTS The RATIO for a normal appendix was 1.32±0.16,while for acute appendicitis it was 1.09±0.07.The cut-off value for RATIO was determined to be≤1.18.The area under the receiver operating characteristic curve(AUC)for diagnosing acute appendicitis using RATIO≤1.18 and MOD>6 mm was 0.870 and 0.652,respectively.There was a significant difference in AUC between RATIO≤1.18 and MOD>6 mm(P<0.0001).When comparing the combination of RATIO≤1.18 and MOD>6 mm with MOD>6 mm alone,the combination showed increased specificity,positive predictive value(PPV),and AUC.However,the sensitivity and negative predictive value decreased.CONCLUSION Combining RATIO of the appendix≤1.18 and MOD>6 mm can significantly improve the specificity,PPV,and AUC in the US diagnosis of acute appendicitis.

Design and synthesis of two coordination polymers for the rapid detection of ciprofloxacin based on triphenylpolycarboxylic acid ligands

Two coordination polymers were synthesized by hydrothermal reaction,namely,[Cd(H_(3)cpbda)(2,2′‑bipy)(H_(2)O)]_(n)(1)and[Mn(H_(3)cpbda)(phen)(H_(2)O)]_(n)(2),where H_(5)cpbda=5,5′‑[(5‑carboxy‑1,3‑phenyl)bis(oxy)]triisophthalic acid,2,2′‑bipy=2,2′‑bipyridine,phen=1,10‑phenanthroline.The two complexes were characterized by single‑crystal X‑ray diffraction,powder diffraction,infrared spectroscopy,and thermogravimetric analysis.Complexes 1 and 2 are“V”‑shaped 1D chains,and the molecules form 2D(1)and 3D framework(2)structures through weakπ…πstacking.Furthermore,complex 1 was dispersed in an aqueous solution and its fluorescence intensity demonstrated excellent stability.Complex 1 can specifically detect ciprofloxacin in urine with a detection limit of 1.91×10^(-8)mol·L^(-1).CCDC:2359498,1;2359499,2.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

An evaluation of ureteral diameter ratio and vesicoureteral reflux index in the treatment of primary vesicoureteral reflux

Objective:Vesicoureteral reflux(VUR)index is a simple,validated tool that reliably predicts significant improvement and spontaneous resolution of primary reflux in children.The aim of this study was to evaluate and compare the ureter diameter ratio(UDR)and VUR index(VURx)of patients treated with endoscopic injection(EI)and ureteroneocystostomy(UNC)methods in the pediatric age group due to primary VUR.Methods:Patients under the age of 18 years old who underwent EI and UNC with the diagnosis of primary VUR between January 2011 and September 2021 were determined as the participants.The UDR was assessed using voiding cystourethrography,and the VURx score was determined prior to treatment based on hospital records included in the study.Results:A total of 255 patients,60(23.5%)boys and 195(76.5%)girls,with a mean age of 76.5(range 13.0e204.0)months,were included in the study.EI was applied to 130(51.0%)patients and UNC was applied to 125(49.0%)patients due to primary VUR.The optimum cut-off for the distal UDR was obtained as 0.17 with sensitivity and specificity of 73.0%and 63.0%,respectively.The positive and negative predictive values were 66.0%and 70.0%,respectively.Conclusion:When the UDR and VURx score are evaluated together for the surgical treatment of primary VUR in the pediatric age group,it is thought that it may be useful in predicting the clinical course of the disease and evaluating surgical treatment options.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

Fluorescence color tuning of dual-emission carbon quantum dots pro-duced from biomass and their use in Fe3+and Cu2+detection

Using simple and eco-friendly ethanol solvothermal treatment,dual-emission biomass carbon quantum dots(D-BCQDs)were synthesized from biomass viburnum awabuki leaves.Under excitation with 413 nm wavelength light two emission peaks appeared at 490 and 675 nm and the dots could be tuned to emit crimson,red,purplish red,purple and blue-gray fluorescence by changing the solvothermal temperature from 140℃ to 160,180,200 and 240℃,respectively.XPS and FTIR characterization in-dicated that the fluorescence color was mainly determined by surface oxidation defects,elemental nitrogen and sp^(2)-C/sp^(3)-C hybrid-ized structural domains.The D-BCQDs could not only detect Fe^(3+)or Cu^(2+),but also quantify the concentration ratio of Fe^(3+)to Cu^(2+)in a solution containing both,demonstrating their potential applications in the simultaneous detection of Fe^(3+)and Cu^(2+)ions.

An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware.These malicious applications have become a serious concern to the security of Android systems.To address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples.However,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification results.The current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training time.The proposed approach consists of two main steps.First,a feature selection method based on the Attention mechanism is used to select the most important features.Then,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the malware.The feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural network.The role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification process.Experimental evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.

The Machine Learning Ensemble for Analyzing Internet of Things Networks:Botnet Detection and Device Identification

The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a result,research on network analysis has become vital.Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods.In this paper,we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework.The results indicate that using the proposed ensemble techniques improves accuracy by up to 1.7%compared to single-algorithm approaches.These results also suggest that the proposed framework can flexibly adapt to general IoT network analysis scenarios.Unlike existing frameworks,which only exhibit high performance in specific situations,the proposed framework can serve as a fundamental approach for addressing a wide range of issues.

A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

Design and prototyping of the readout electronics for the transition radiation detector in the high energy cosmic radiation detection facility

The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals.

A Glassy Carbon Electrode Modified with Cellulose Nanofibrils from Ammophila arenaria for the Sensitive Detection of L-Trytophan

L-tryptophan is an essential amino acid for human health. Nanofibrillated cellulose (NFC) from marram grass (Ammophila arenaria) extracted from plants harvested in the center of Tunisia was used for the first time for the modification of a glassy carbon electrode (GCE), for the sensitive detection of L-tryptophan (Trp). After spectroscopic and morphological characterization of the extracted NFC, the GC electrode modification was monitored through cyclic voltammetry. The NFC-modified electrode exhibited good analytical performance in detecting Trp with a wide linear range between 7.5 × 10−4 mM and 10−2 mM, a detection limit of 0.2 µM, and a high sensitivity of 140.0 µA∙mM−1. Additionally, the NFC/GCE showed a good reproducibility, good selectivity versus other amino acids, uric acid, ascorbic acid, and good applicability to the detection of Trp in urine samples.

The Diagnostic Value of Oligoclonal Band Detection in Viral Encephalitis

Objective: This study aims to explore the differences in cerebrospinal fluid oligoclonal band (CSF-OCB) expression among different age groups in viral encephalitis and its reference value for diagnosis. Methods: Forty-two patients with viral encephalitis were divided into two groups: 25 adults and 17 children. The presence of oligoclonal bands in the cerebrospinal fluid (CSF) was detected using polyacrylamide gel electrophoresis, and CSF routine analysis was conducted for comparative analysis. Results: The CSF-OCB positivity rate was higher in the adult group (48%) compared with the pediatric group (11.76%), with a statistically significant difference (P Conclusion: 1) The expression of CSF-OCB positivity in patients with viral encephalitis is age-related, with higher positivity rates observed in adults compared to children. 2) Although CSF oligoclonal band detection is not a specific diagnostic marker for viral encephalitis in adults, it still holds certain reference value.

Study of the Characteristics of Large-Diameter Iron Bars Obtained by Rolling at the ODHAV Foundry in the Republic of Guinea

This work consists of evaluating the quality of the mechanical parameters of large-diameter steels, i.e. 20, 25, 28 and 32, through a process of recycling scrap metal that fills garages, rubbish dumps, gutters and other abandoned sites, as well as imported concrete reinforcing steel sold in the Republic of Guinea. To carry out this important work, a number of mechanical tensile and bending tests and a microscopic analysis combining two devices, an electron microscope and a photographic camera, were carried out. The samples were taken from sampling areas in the major communes of Conakry, namely: Casse Sonfonia, Matoto and Kagbélen. The tensile strength values of the large dimensions 20, 25, 28 and 32 are given in the tables.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.