SACOC: A spectral-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Pembelajaran Pemrograman Berorientasi Objek (Object Oriented Programming) Berbasis Project Based Learning

 Learning aims to help to gain experience both knowledge, skills, and values ​​in order to become increasing quantity and quality. For that education should be able to prepare human resources creative. The problems found in learning require creative thinking especially in programming learning, the ability to think algorithm is needed to make the program made as expected. Object oriented programming or Object Oriented Programming (OOP) provides convenience in making a program, because OOP programming has been using the concept of modularity of objects and classes. Java is an object oriented programming (OOP) programming language that can run on various operating system platforms, both on computers and on mobile phones. Alice can be used for Object-based programming learning, as Alice is a program designed to learn the basic concepts of computer programs while creating story telling and simple 3D interactive games. Alice can introduce the concept of fun programming through learning to create animations and games. Simply project-based learning using Alice can be applied with learning using animation technology with daily life problems. Project-based learning (project based learning) methods make it more active, creative and successful in solving problems with good and correct algorithms. The project-based learning model has advantages in improving learning outcomes and motivation.   Keywords: Alice, Object oriented programming, Project based learnin

Paleogene dinoflagellate cysts and thermal maturity from Pabdeh Formation ( Zagros basin, west of Iran)

Palynological investigation on 132 samples from the 428m thick outcrop section of Late Paleocene to Early  Oligocene of the Pabdeh Formation in southwestern Iran yielded 55 species of dinoflagellate cysts and allowed  establishment of seven biozones. Quantity of marine palynomorph elements indicate an open marine  environment at that time interval but, a slight increase in number of spore and pollen grains in some samples  indicate suitable conditions for forest development as a consequence of increase in climate humidity. The  species are common in various latitudes and most of them are cosmopolitan. Thermal maturity index  measurement indicates oil prone nature for majority of the samples.Keyword: Pabdeh Formation, Dinoflagellate cysts, Paleogene, Palynostratigraphy, Thermal maturit

MACOC: a medoid-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Multiple Linear Regression and Machine Learning for Predicting the Drinking Water Quality Index in Al-Seine Lake

This is the final version. Available on open access from MDPI via the DOI in this recordData Availability Statement: The data sets are available from the corresponding author on reasonable request.Ensuring safe and clean drinking water for communities is crucial, and necessitates effective tools to monitor and predict water quality due to challenges from population growth, industrial activities, and environmental pollution. This paper evaluates the performance of multiple linear regression (MLR) and nineteen machine learning (ML) models, including algorithms based on regression, decision tree, and boosting. Models include linear regression (LR), least angle regression (LAR), Bayesian ridge chain (BR), ridge regression (Ridge), k-nearest neighbor regression (K-NN), extra tree regression (ET), and extreme gradient boosting (XGBoost). The research’s objective is to estimate the surface water quality of Al-Seine Lake in Lattakia governorate using the MLR and ML models. We used water quality data from the drinking water lake of Lattakia City, Syria, during years 2021–2022 to determine the water quality index (WQI). The predictive performance of both the MLR and ML models was evaluated using statistical methods such as the coefficient of determination (R2) and the root mean square error (RMSE) to estimate their efficiency. The results indicated that the MLR model and three of the ML models, namely linear regression (LR), least angle regression (LAR), and Bayesian ridge chain (BR), performed well in predicting the WQI. The MLR model had an R2 of 0.999 and an RMSE of 0.149, while the three ML models had an R2 of 1.0 and an RMSE of approximately 0.0. These results support using both MLR and ML models for predicting the WQI with very high accuracy, which will contribute to improving water quality management

Self-rated health in Pakistan: results of a national health survey

BACKGROUND: Self-rated health (SRH) is a robust predictor of mortality. In UK, migrants of South Asian descent, compared to native Caucasian populations, have substantially poorer SRH. Despite its validation among migrant South Asian populations and its popularity in developed countries as a useful public health tool, the SRH scale has not been used at a population level in countries in South Asia. We determined the prevalence of and risk factors for poor/fair SRH among individuals aged ≥15 years in Pakistan (n = 9442). METHODS: The National Health Survey of Pakistan was a cross-sectional population-based survey, conducted between 1990 and 1994, of 18 135 individuals aged 6 months and above; 9442 of them were aged ≥15 years. Our main outcome was SRH which was assessed using the question: "Would you say your health in general is excellent, very good, good, fair, or poor?" SRH was dichotomized into poor/fair, and good (excellent, very good, or good). RESULTS: Overall 65.1% respondents – 51.3 % men vs. 77.2 % women – rated their health as poor/fair. We found a significant interaction between sex and age (p < 0.0001). The interaction was due to the gender differences only in the ages 15–19 years, whereas poor/fair SRH at all older ages was more prevalent among women and increased at the same rate as it did among men. We also found province of dwelling, low or middle SES, literacy, rural dwelling and current tobacco use to be independently associated with poor/fair SRH. CONCLUSION: This is the first study reporting on poor/fair SRH at a population-level in a South Asian country. The prevalence of poor/fair health in Pakistan, especially amongst women, is one of the worst ever reported, warranting immediate attention. Further research is needed to explain why women in Pakistan have, at all ages, poorer SRH than men

A study of fluid overpressure microstructures from the creeping segment of the San Andreas fault

Evidence of episodic fluid overpressure events noted in samples from the San Andreas Fault Observatory at Depth (SAFOD) have remained largely uncorrelated in terms of their collective significance for seismic history of the fault zone. The compositional and microstructural correlations sought in this study could shed light on questions about potential for major seismic events in the creeping segment of the SAF in central California. We used quantitative energy dispersive spectroscopy (EDS), Cathodoluminescence (CL) and Scanning Electron Microscope (SEM) imaging, and electron backscatter diffraction (EBSD) analysis to acquire geochemical and microstructural data from a suite of twenty SAFOD core samples including the damage zone and the active core of the fault. The results indicate intermittent coseismic fluid overpressure events that overprint the background aseismic creep across the fault. Analysis of trace elements and deformation in the coseismic calcite vein generations and their associated hydrothermal mineral phases indicate progressive uplift and exhumation followed by an asymmetric incursion of meteoric water into the damage zone. The same analysis suggests that the actively creeping intervals act as permeability barriers. Our results are in overall agreement with recent studies of the SAF in central California that indicate large seismic events have occurred intermittent with aseismic creep in recent geological time or suggest future potential for such events