Classification Parameters Research Articles

An evaluation on the parameters used in flood susceptibility analysis

Taşkınlar her geçen gün artan büyüklük ve sıklıklarına bağlı olarak dünyada ve ülkemizde önemi giderek artan afetlerden birisidir. Bu çalışmadaki temel amaç, taşkın duyarlılık ile ilgili uluslararası ve ulusal literatürün değerlendirilmesi ve duyarlılık çalışmalarına yeni bir yaklaşım olarak sel ve taşkınların meydana geldiği yerleşmelerin su toplama havzaları temelli taşkın duyarlılık parametrelerinin belirlenmesini gerçekleştirmektir. Bu kapsamda çalışmada tarihsel taşkın envanterine bağlı olarak Bursa ili sınırları içerisinde vadi tabanı ve akarsu kenarında sel ve taşkınların yaşandığı yerleşmelerin havzalarına bağlı olarak taşkın duyarlılık analizi parametreleri belirlenmiştir. Çalışmada kullanılan temel altlık veriler, Bursa iline ait 5m çözünürlüklü Sayısal Yükseklik Modeli (SYM), 1956-2022 yılları arasını kapsayan envanter verileri, litoloji, hidrolojik toprak grupları (HTG) ve yağış (WorldClim) verileridir. Bursa il sınırları içerisinde meydana gelen tarihsel sel ve taşkın envanterine bağlı olarak 28 yerleşme ve bu yerleşmelerin su toplama havzaları belirlenmiş ve bu havzalara sel ve taşkının oluşmasında hazırlayıcı 12 parametre uygulanmıştır. Taşkın hazırlayıcı parametreler sınıflandırma aşamasında 0-1 arasında normalize edilerek ortaya çıkan sonuca göre taşkın duyarlılık için parametre katsayıları oluşturulmuştur. Sonuç olarak envantere göre maksimum etkiye sahip parametreler; çatallanma oranı (R_b), drenaj yoğunluğu (D_d), akım toplanma zamanı (T_c), eğim, topografik nemlilik indeksi, akarsu güç indeksi, hidrolojik toprak grupları, olarak belirlenmiştir. Bu çalışma ile taşkın duyarlılık analizinde önceki çalışmalardan farklı olarak envantere bağlı ve yerleşim temelli havzalardan taşkın duyarlılık parametreleri belirlenmiştir.

Hyperspectral image classification using a new deep learning model based on pseudo-3D block and depth separable 2D–3D convolution

In the task of classification of hyperspectral images (HSI), standard 2D and 3D convolutions increase the number of trainable parameters, making the model computationally complex, prone to overfitting when trained on smaller datasets, and demanding a large amount of memory. Thus, the cost of computation and memory consumption are bottlenecks of deep hybrid 2D–3D CNN models. This inspired us to propose a light weighted hybrid CNN model with fewer trainable parameters for efficient and accurate classification of HSI by introducing the concept of pseudo-3D block and depth separable 2D–3D convolution. The major components of the proposed framework are standard 3D-CNN, pseudo-3D block, depthwise separable 3D CNN, depth separable 2D CNN, and spatial pyramid pooling (SPP). Pseudo-3D block and depth separable 2D–3D convolution make the model computational and memory efficient by drastically reducing the number of trainable parameters. Further, the SPP layer is placed between the convolution layers and fully connected layers to generate a feature vector of fixed length at different scales. The proposed model is applied to three publicly available datasets. Experimental analysis proves the efficacy of the proposed approach over the state-of-the-art methods.

Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery

Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hyperparameter tuning was performed to identify the best parameters for a pixel-based classification. The findings demonstrate the superiority of multispectral data for burnt area and burn severity classification with both RF and SVM models. While the RF model achieved a 95.5% overall accuracy for the burnt area classification using RGB data, the RGB models encountered challenges in distinguishing between mildly and severely burnt classes in the burn severity classification. However, the RF model incorporating mixed data (RGB and multispectral) achieved the highest accuracy of 96.59%. The outcomes of this study contribute to the understanding and practical implementation of machine learning techniques for assessing and managing burnt areas.

Keeping It Simple: Developing a Prognostic Tool for Spinal Epidural Abscess.

A retrospective study. To develop a prognostic score for mortality and treatment failure in Spinal epidural abscess (SEA), based on simplicity and multidimensional assessment principles. One-hundred-fifty patients were reviewed. Variables assessed included comorbidities, functional status, clinical presentation, Frankel classification, and biochemical and radiological parameters. The main outcomes were the 90-day mortality and treatment failure, corresponding to any intensification of the initial treatment plan. Variables were sorted out with a factorial analysis. Logistic regressions were performed, and the new score was derived from the coefficients. ROC curves with Area Under Curve, calibration plots, and cross-validation were performed. Forty-three patients (29%) had treatment failure, and 15 died (10%) by 90days. Factorization created 3 groups: Comorbidities (C), Severity (S), and Function (F). For 90-day mortality, Odds ratios were 1.20 (P = .0002), 1.15, (P = .03), 1.36, (P < 10-4) for C, S, F, respectively. The new score 'CSF' had 1 point per item, ranging from zero to 3. OR increased by 1.2/point for 90-day mortality (P < 10-4), AUC was .86. For failures OR increased by 1.15/point (P = .014), AUC was .58, and increased to .64 for patients who survived after 90 days, probably due to competing risks. Comorbidities, Severity, and Function is a new simplistic tool, easy to use in daily practice; its performances were excellent for 90-day mortality, and acceptable for failures. Simple tools are more likely to be adopted into practice. External validation of this technique is desirable.

Bayesian penalization for explanatory cognitive diagnostic models: covariate DINA model and covariate LCDM with the lasso prior

AbstractDiagnostic assessment data obtained from online learning platforms for schools are typically accompanied by student background variables and item responses. To leverage such information for cognitive diagnosis, the present study examines the applicability of the lasso prior for variable selection in explanatory cognitive diagnostic models (ECDMs) with attribute-level explanatory variables. We proposed the covariate log-linear cognitive diagnostic model (LCDM) and covariate deterministic input, noisy, and gate (DINA) model and compared the models with and without the lasso prior using a real-world data analysis and a simulation study. In the real-world data analysis, which used a school-sized sample collected from an online learning platform, we found that the lasso prior selected only relatively large effects without substantially affecting the diagnostic classification and item parameter estimation. In the simulation study, we found that the lasso prior did not degrade the accuracy of the diagnostic classification or parameter estimation. Finally, we discuss the situations in which the lasso prior can be useful with the ECDMs.

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Abstract Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.

DFS-WR: A novel dual feature selection and weighting representation framework for classification

Classification methods relying on multidimensional features have been widely applied in commerce, healthcare, and transportation. Nevertheless, haphazardly selecting inappropriate features may not only hinder the improvement of classification performance but also disrupt the convergence of classifier parameters, leading to prolonged classifier runtime and an increased storage burden. Therefore, this paper proposed a novel framework (dubbed as DFS-WR) to perform dual selection and weighted representation of multidimensional features. Firstly, DFS-WR employs Laplacian scores to conduct a de-redundant operation as the first feature selection. Subsequently, DFS-WR utilizes a correlation coefficient matrix and redundant sorting to eliminate duplicate deployment of similar features in classification through the second selection. Finally, for hierarchical expression, DFS-WR assigns weights by virtue of information gain (IG), thereby emphasizing the significance of dominant features. The performance of DFS-WR was further evaluated across four aspects: classification ability, runtime performance, storage occupancy, and portability and universality, using five public datasets and eleven widely adopted classifiers. Additionally, this paper considered the proper configuration of crucial parameters within DFS-WR and compared it with other related works. The results indicated that the dual selection strategy for eliminating redundancy and similarities among features could improve the performance of classifiers, and the weighted process based on IG outperformed unweighted and ReliefF-based weighted methods. By employing DFS-WR to multidimensional features, the classifier’s runtime and data storage capacity were effectively reduced. In addition, it was proved that the deployment of DFS-WR generally had no limitations on datasets and classifiers, underscoring its good portability and universality in practical applications.

Effective acoustic parameters for automatic classification of performed and synthesized Guzheng music

This study focuses on exploring the acoustic differences between synthesized Guzheng pieces and real Guzheng performances, with the aim of improving the quality of synthesized Guzheng music. A dataset with consideration of generalizability with multiple sources and genres is constructed as the basis of analysis. Classification accuracy up to 93.30% with a single feature put forward the fact that although the synthesized Guzheng pieces in subjective perception evaluation are recognized by human listeners, there is a very significant difference to the performed Guzheng music. With features compensating to each other, a combination of only three features can achieve a nearly perfect classification accuracy of 99.73%, with the essential two features related to spectral flux and an auxiliary feature related to MFCC. The conclusion of this work points out a potential future improvement direction in Guzheng synthesized algorithms with spectral flux properties.

Study of Tunnel Support Methods Based on RMR and Q-System Methods at Kerinci Merangin Hydropower Jambi Province

Excavation of tunnel construction at the Kerinci Merangin Hydropower in Jambi Province is a complex job, because it will use the tunnel as a waterway which will always be faced with unstable conditions. One of the way to deal with instability is to classify the mass and determine the type of support on the roof and walls using empirical methods. The parameters in rock mass classification include mapping the structure, physical and mechanical properties of the material. Empirical analysis was carried out using the Rock Mass Rating (RMR) classification and the Q-System to determine the condition of the rock mass in the tunnel and determine the type of support that will be used and in accordance with the rock mass class. The rock mass classes in the study area of STA 8+869, 8+872 and 8+879 based on the RMR classification are included in rock class III, namely rocks that have moderate conditions (fair quality rock mass) and rock class IV, namely rocks that have poor conditions (poor quality rock mass). Meanwhile, the rock mass class based on the Q-System includes very poor rock mass.The recommendation for supporting the tunnel face according to RMR in the study area is to use a combination of rockbolt, shotcrete and wiremesh supports, while the support recommendation according to the Q-System uses a combination of rockbolt and shotcrete supports.

Localization of HIFS in primary distribution networks using voltage and current sequence components

In the power system protection when a conductor accidentally makes contact with a highly resistive medium, a HIF problem arises. Because of their lowest current amplitude and incredibly varied features, the HIF are challenging to detect. To detect the fault type this work proposes Support Vector Machine (SVM) classifier and Convolution Neural Network (CNN) classifier to localize the detected fault based on the trained statistical features attained from current and voltage sequence components at distribution feeders. The weight parameters of the SVM classifier and the CNN classifiers are tuned by a novel Hybrid AqSea Optimization Algorithm. The performance of the presented HIFD method is compared to assess proficiency. The adopted model at 70 % learning attains an accuracy of 93 %, which is 0.89 %, 7.69 %, 4.60 %, 1.21 % and 30.37 %, better than the previous models like SVM, SVM + GWO, SVM + DFA, SVM + AQO, SVM + SOA, and Aqsea + SVM, respectively.

Semisupervised Deep Features of Time-Frequency Maps for Multimodal Emotion Recognition

Traditional approaches for emotion recognition utilize unimodal physiological signals. The effectiveness of such systems is affected by some limitations. To overcome them, this paper proposes a new method based on time-frequency maps that extract the features from multimodal biological signals. At first, the fusion of electroencephalogram (EEG) and peripheral physiological signal (PPS) is performed, and then, the two-dimensional discrete orthonormal Stockwell transform (2D-DOST) of the multimodal signal matrix is calculated to obtain time-frequency maps. A convolutional neural network (CNN) is then utilized to extract the local deep features from the absolute output of the 2D-DOST. Since there are uninformative deep features, the semisupervised dimension reduction scheme reduces them by balancing the generalization and discrimination. Finally, the classifier recognizes the emotion. The Bayesian optimizer finds the proper SSDR and classifier parameter values to maximize the recognition accuracy. The performance of the proposed method is evaluated on the DEAP dataset considering the two- and four-class scenarios through extensive simulations. This dataset consists of electroencephalograph (EEG) signals in 32 channels and peripheral physiological signals (PPSs) in eight channels from 32 subjects. The proposed method reaches the accuracy of 0.953 and 0.928 for two- and four-class scenarios, respectively. The results indicate the efficiency of the multimodal signals for detecting emotions compared to that of unimodal signals. Also, the results indicate that the proposed method outperforms the recently introduced ones.

Mechanical outcomes of the TFNA, InterTAN and IMHS intramedullary nailing systems for the fixation of proximal femur fractures

IntroductionHip fractures have become a major public health priority due to their increasing incidence. Intramedullary (IM) nailing has gained popularity as a surgical technique for managing these fractures. However, comparative studies with extramedullary devices indicate potentially increased mechanical complications associated with IM nailing. Specifically, there have been recent reports of early implant fracture associated with the TFNA system. This study compares the rate of mechanical complications between 3 implants. Secondarily, we analysed the factors associated with implant fracture and all other mechanical complications. MethodsThis retrospective study included 803 IM nails performed between 2008–2021 for the fixation of proximal femur fractures at a level 1 trauma centre. We recorded patient demographics, AO fracture classification, implant specification and intra-operative parameters such as tip-apex distance, Cleveland index, reduction quality, presence of medial calcar integrity, neck-shaft angle. The outcomes analysed included implant fracture, lag screw cut-out, >10 mm backing out, autodynamisation, periprosthetic fracture, non-union at 6 months and time to revision surgery. ResultsThe overall implant fracture rate was 3.1 % (25/803), with no statistically significant difference between the three devices (p = 0.51). The TFNA group included 183 patients with a median follow-up of 1.6 years. The TFNA fracture rate was 2.2 % (4/183) which occurred at 207 +/- 16.66 days post-operatively. The rate of all mechanical complications was 4.9 % in the TFNA group, 12.9 % in the InterTAN group and 17 % in the IMHS group. Non-union (p < 0.001) and increasing lag screw length (p < 0.02) were identified as risk factors for implant fracture and other mechanical complications. The reverse oblique fracture pattern (AO 31 A3.1) was associated with mechanical complications only (p < 0.01). The cumulative survival rates without revision for the TFNA, InterTAN, IMHS groups were 97.8 %, 95.5 % and 87.9 % respectively at 2.5 years. DiscussionThe rates of implant fracture were comparable between the three nailing systems. The TFNA provides effective fixation for proximal femur fractures using a traditional screw without cement augmentation. The TFNA was associated with an overall reduction in mechanical complications compared to the InterTAN and IMHS. Previously reported mechanical complications associated with IM Nails may relate to older device designs.

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.

Use of self-extendable mesh in the minimally invasive treatment of posterolateral diaphragmatic hernia, a novel device

BackgroundCongenital diaphragmatic hernia (CDH) is a pathology found in neonates, though it can affect individuals of various ages. The definitive treatment for this condition is surgical, either through an open or minimally invasive approach. Hernial defects not allowing primary closure must be corrected with a mesh. There is no specific mesh for this pathology. We describe the use of a self-extendable mesh based on a market-available mesh for the closure of a CDH that facilitates its placement and suture to the diaphragm using a minimally invasive technique. MethodsWe conducted a retrospective longitudinal study from January 2019 to January 2023. The study included patients under the age of 18 with a confirmed diagnosis of CDH who underwent minimally invasive surgery with the placement of a self-extendable mesh. A minimum follow-up period of six months was established. Descriptive statistics were applied to analyze various variables, including age at diagnosis, associated pathologies, hernia classification, surgical time, and postoperative parameters, such as complications, respiratory and gastrointestinal symptoms, recurrence, and thoracic alterations. ResultsWe included five patients with CDH. Two patients were diagnosed in the neonatal period, two between 28 days of life and one year, and one after the first year of life. All patients had posterolateral hernias; three were left-sided, and two were right-sided. Four had a type C defect, and one had a type B defect. All patients were treated by minimally invasive surgery, through thoracoscopy, with a surgical time between 89 and 120 min. There were no intraoperative complications or conversions to open surgery. Short and medium-term follow-up was performed in the remaining patients, the shortest follow-up period being 19 months and the longest reaching 36 months. No recurrence of the defect or thoracic or vertebral deformities were reported. ConclusionsThe modified self-extendable mesh is a device that offers a safe and effective way of correcting CDH, facilitating the placement and suture process to the native tissue while reducing the risk of injury to the abdominal viscera. Although we cannot definitively conclude that the surgical time decreases based on our limited number of cases, the mesh shows promise as a valuable tool in treating CDH. Further research and a larger sample are necessary to determine the effects on surgical time.

Novel MobileNet based Multipath Convolutional Neural Network for defect detection in fabrics

Automatic fabric defect detection and classification is the most important process in the textile industry to ensure the fabric quality. In the existing systems, a learning based method is used for detecting defects in plain weave fabrics. In this paper, a novel MobileNet based Multipath Convolutional Neural Network (MMPCNN) architecture is proposed for detection and classification of simple and complex patterned fabric defects. In the proposed MMPCNN architecture, MobileNet model is used in the first path. In this, Gabor filter bank is used instead of conventional filters in the first convolution layer. A simple convolutional neural network architecture with Gray Level Co-occurrence Matrix (GLCM) features as an input is used in the second path of the MMPCNN architecture. Gabor filters are more useful for analyzing the texture with different orientations and scales. Each Gabor filter parameter has its own impact on analyzing the texture and extracting the information from the texture. Therefore, in this paper, the use of Gabor filter parameters in MMPCNN architecture is analyzed. The proposed model is experimented on the TILDA textile image database and it is able to achieve 100% accuracy with reduced trainable parameters for fabric defect detection and classification.

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.

Brain blood flow pulse analysis may help to recognize individuals who suffer from hydrocephalus

BackgroundNormal pressure hydrocephalus (NPH) is often associated with altered cerebral blood flow. Recent research with the use of the ultrasonic method suggests specific changes in the shape of cardiac-related cerebral arterial blood volume (CaBV) pulses in NPH patients. Our study aims to provide a quantitative analysis of the shape of CaBV pulses, estimated based on transcranial Doppler ultrasonography (TCD) in NPH patients and healthy individuals.MethodsThe CaBV pulses were estimated using TCD cerebral blood flow velocity signals recorded from probable NPH adults and age-matched healthy individuals at rest. The shape of the CaBV pulses was compared to a triangular shape with 27 similarity parameters calculated for every reliable CaBV pulse and compared between patients and volunteers. The diagnostic accuracy of the most prominent parameter for NPH classification was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsThe similarity parameters were calculated for 31 probable NPH patients (age: 59 years (IQR: 47, 67 years), 14 females) and 23 healthy volunteers (age: 54 years (IQR: 43, 61 years), 18 females). Eighteen of 27 parameters were different between healthy individuals and NPH patients (p < 0.05). The most prominent differences were found for the ascending slope of the CaBV pulse with the AUC equal to 0.87 (95% confidence interval: 0.77, 0.97, p < 0.001).ConclusionsThe findings suggest that in NPH, the ascending slope of the CaBV pulse had a slower rise, was more like a straight line, and generally was less convex than in volunteers. Prospective research is required to verify the clinical utility of these findings.

Permittivity-Based Microparticle Classification by the Integration of Impedance Cytometry and Microwave Resonators.

Permittivity of microscopic particles can be used as a classification parameter for applications in materials and environmental sciences. However, directly measuring the permittivity of individual microparticles has proven to be challenging due to the convoluting effect of particle size on capacitive signals. To overcome this challenge, we built a sensing platform to independently obtain both the geometric and electric size of a particle, by combining impedance cytometry and microwave resonant sensing in a microfluidic chip. This way the microwave signal, which contains both permittivity and size effects, can be normalized by the size information provided by impedance cytometry to yield an intensive parameter that depends only on permittivity. The technique allowed us to differentiate between polystyrene and soda lime glass microparticles -below 22 microns in diameter- with more than 94% accuracy, despite their similar sizes and electrical characteristics. Furthermore, we show that the same technique can be used to differentiate between normal healthy cells and fixed cells of the same geometric size. The technique offers a potential route for targeted applications such as environmental monitoring of microplastic pollution or quality control in pharmaceutical industry. This article is protected by copyright. All rights reserved.

Cancer Disease Treatment Classification Using Extended Rough based Intuitionistic Fuzzy C-means Learning Methodology

Because of the sensitivity of magnetic resonance to noise for the diagnosis and study of cancer, classifying magnetic resonance disease treatments is a medically complex and vitally important process. To sum up, these are the most pressing problems with the current system for describing how to treat cancer. Mathematical methods like rough sets, fuzzy sets, and rough sets are utilized to assess and cope with the ambiguity and uncertainty present in medical cancer disease treatments. Rough sets and rough sets-based techniques have both been offered in past, but depending on the parameters, each has its own special set of issues. The Extended Rough based Intuitionistic Fuzzy C-means Learning Technique (ERIFCM) with estimation of weight bias parameter for Cancer Disease Treatment Classification presented in this work is a novel method for computing the disease treatment classification. Using the non-membership and membership values in intuitionistic Rough based fuzzy sets, a generalised kind of fuzzy, rough sets and their representative components are evaluated. The approach that is proposed in this research uses existing clustering's standard features to lower the intensity and noise associated with cancer treatments without the need for spatial weight context data. In addition, the cluster centroid is initialized using the max-dist evaluation method, which is based on the weight measure, before the proposed algorithm is executed. This helps to reduce the number of iterations required for clustering. In contrast to existing segmented approaches developed for Cancer Disease Treatment and similar Disease Treatments, experimental results of the proposed approach yielded effective Disease Treatment Classification results. The main component of the proposed method is a more thorough analysis of experimental results.