Preliminary Steps Toward Extracting the Specific Alternative Model for Personality Disorders Diagnoses From Criteria A and B Self-Reports

The Alternative DSM-5 Model for Personality Disorders (AMPD) retains six specific personality disorders (PDs) that can be diagnosed based on Criterion A level of impairment and Criterion B maladaptive facets. Those specific diagnoses are still underresearched, despite the preference expressed by most PD scholars for a mixed/hybrid classification. This study explores the possibility of using Criterion A and B self-report questionnaires to extract the specific AMPD diagnoses. Plausible prevalence estimates were found in three samples (outpatient PD, private practice, community; N = 766) using the facet score > 2 and t score > 65 methods for determining the presence of a Criterion B facet; diagnoses had meaningful correlations with external variables. This study provides evidence—albeit preliminary—that the extraction of the specific AMPD PDs from self-report questionnaires might be a viable avenue. Ultimately, it could promote the use and dissemination of those diagnoses for screening purposes in clinical and research settings.

Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.

A Hybrid Classification Technique for Enhancing the Effectiveness of Intrusion Detection Systems Using Machine Learning

The objective of this paper is to propose and develop a hybrid intrusion detection system to handle series and non-series data by applying the two different concepts that are named clustering and autocorrelation function in a single architecture. There is a need to propose and build a system that can handle both types of data whether it is series or non-series. Therefore, the authors used two concepts to generate a robust approach to craft a hybrid intrusion detection system. The authors utilize an unsupervised clustering approach that is used to categorize the data based on domain similarity to handle non-series data and another approach is based on autocorrelation function to handle series data. The approach is consumed in single architecture where it carries data as input from both host-based intrusion detection systems and network-based intrusion detection systems. The result shows that the hybrid intrusion detection system is categorizing data based on the optimal number of clusters obtained through the elbow method in clustering.

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

Mapping Environmental Impacts of Rapid Urbanisation and Deriving Relationship between NDVI, NDBI and Surface Temperature: A Case Study

Urbanisation is a complex global phenomenon driven by unorganised expansion, increased immigration, and population explosion. Changes in land cover are one of the most critical components for managing natural resources and monitoring environmental impacts in this context. In the present study, a hybrid classification approach was applied to Landsat data to get insight into the urbanisation of the Chandigarh capital region from 2000 to 2020. The results demonstrate an increasing urbanisation tendency on the city’s outskirts, particularly in the north-western and southern directions. The most considerable alterations were seen in the class vegetation as it swiftly transformed to built-up regions. Two indices, namely NDVI and NDBI and surface temperature images, were also derived from studying their inter-relationships. The paper suggests a positive linear relationship between surface temperature and NDBI while a negative correlation between NDVI and NDBI. Such studies may help city planners to take timely and appropriate efforts to reduce the environmental consequences of urbanisation.

A Review Article on Brain Tumor Detection and Optimization using Hybrid Classification Algorithm

This review focuses on different imaging techniques such as MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Keywords: Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.

Hybrid Classification for Face Spoof Detection

ML (machine learning) is consisted of a method of recognizing face. This technique is useful for the attendance system. Two sets are generated for testing and training phases in order to segment the image, to extract the features and develop a dataset. An image is considered as a testing set; the training set is contrasted when it is essential to identify an image. An ensemble classifier is implemented to classify the test images as recognized or non-recognized. The ensemble algorithm fails to acquire higher accuracy as it classifies the data in two classes. Thus, GLCM (Grey Level Co-occurrence Matrix) is projected for analyzing the texture features in order to detect the face. The attendance of the query image is marked after detecting the face. The simulation outcomes revealed the superiority of the projected technique over the traditional methods concerning accuracy. Keywords: DWT, GLCM, KNN, Decision Tree