Novelty circular neighboring technique using reactive fault tolerance method

The availability of the data in a distributed system can be increase by implementing fault tolerance mechanism in the system. Reactive method in fault tolerance mechanism deals with restarting the failed services, placing redundant copies of data in multiple nodes across network, in other words data replication and migrating the data for recovery. Even if the idea of data replication is solid, the challenge is to choose the right replication technique that able to provide better data availability as well as consistency that involves read and write operations on the redundant copies. Circular Neighboring Replication (CNR) technique exploits neighboring policy in replicating the data items in the system performs well with regards to lower copies needed to maintain the system availability at the highest. In a performance analysis with existing techniques, results show that CNR improves system availability by average 37% by offering only two replicas needed to maintain data availability and consistency. The study demonstrates the possibility of the proposed technique and the potential of deploying in larger and complex environment

An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning

Worldwide, water security is adversely affected by factors such as population growth, rural–urban migration, climate, hydrological conditions, over-abstraction of groundwater, and increased per-capita water use. Water security modeling is one of the key strategies to better manage water safety and develop appropriate policies to improve security. In view of the growing global demand for safe water, intelligent methods and algorithms must be developed. Therefore, this paper proposes an integrated interval type-2 Fuzzy VIseKriterijumska Optimizcija I Kompromisno Resenje (IT2FVIKOR) with unsupervised machine learning (ML). This includes IT2FVIKOR for ranking and selecting a set of alternatives. Unsupervised machine learning includes hierarchical clustering, self-organizing map, and autoencoder for clustering, silhouette analysis and elbow method to find the most optimal cluster count, and finally Adjusted Rank Index (ARI) to find the best comparison within two clusters. This proposed integrated method can be divided into a two-phase fuzzy-machine learning-based framework to select the best water security strategies and categorize the polluted area using the water datasets from the Terengganu River, one of Malaysia’s rivers. Phase 1 focuses on the IT2FVIKOR method to select five different strategies with five different criteria using five decision makers for finding the best water security strategies. Phase 2 continues the unsupervised machine learning where three different clustering algorithms, namely, hierarchical clustering, self-organizing map, and autoencoder, are used to cluster the polluted area in the Terengganu River. Silhouette analysis is applied along with the clustering algorithms to estimate the number of optimal clusters in a dataset. Then, the ARI is applied to find the best comparison within the original data with hierarchical clustering, self-organizing map, and autoencoder. Next, the elbow method is applied to double-confirm the best clusters for each clustering algorithm. Last, lists of polluted areas in each cluster are retrieved. Finally, this 2-phase fuzzy-Machine learning–based framework offers an alternative intelligent model to solve the water security problems and find the most polluted area

A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario

An extended Interval Type-2 Fuzzy VIKOR technique with equitable linguistic scales and Z-Numbers for solving water security problems in Malaysia

Interval Type-2 Fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (IT2FVIKOR) technique is one of the techniques of Interval Type-2 Fuzzy Multi-Criteria Decision Making (IT2FMCDM), which was developed to solve problems involving conflicting and multiple objectives. Most of the IT2FVIKOR methods are created from linguistic variables based on Interval Type-2 Fuzzy Set (IT2FS) and its generalization, such as Interval Type-2 Fuzzy Numbers (IT2FNs). Recent literature suggests that equitable linguistic scales can offer a better alternative, particularly when IT2FSs have some limitations in handling uncertainty and imbalance. This paper proposes the extended IT2FVIKOR with an equitable linguistic scale and Z-Numbers, where its linguistic scale introduces an equitable balance of positive and negative scales added to the restriction and reliability approach. Different from the typical IT2FVIKOR, which directly utilizes IT2FNs with a positive membership, the proposed method introduces positive and negative membership where each side considers a restriction and reliability approach. Besides, this paper also offers objective weights using fuzzy entropy-based IT2FS to calculate the weights of the extended IT2FVIKOR. The obtained solutions would help decision makers (DMs) identify the best solution to enhance water security projects in terms of finding the best strategies for water supply security in Malaysia

River quality classification using different distances in k-nearest neighbors algorithm

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality

The use of laddle furnace slag in the treatment of contaminants water in acid mine drainage

Once acid drainage is created it eventually flow over streams and rivers, thus, impact negatively on the quality of water bodies. This study aims to treat the water resources that contaminated with acid mine drainage (AMD) with various materials such as ladle furnace slag (LFS), bentonite and zeolite. Major element content in treatment materials were analyzed by X-Ray Fluorescence (XRF). The surface area is determined by using Ethylene Glycol Methyl Ether (EGME). Surface morphology of the sample was performed by Scanning Electron Microscopic (SEM). The pH value was determined by Hanna Instrument and heavy metal concentration was determined by ICP-MS using standard method and Batch Equilibrium Test (BET) for adsorption ability for treatment materials to remove heavy metals. LFS has the highest CaO content which is 55.96% compared to bentonite and zeolite, 2.05 % and 3.48% respectively. Specific surface area (SSA) for LFS is the highest value of 980.45 m²/g. From the adsorbent test, LFS has decreased the Pb ion from 707.47 mg/L to 3.81 mg/L. LFS has been chosen as the best adsorbent compared to the other material due to its high potential to neutralize pH as well as has great adsorption capacity in removal Pb metal ions up to 99.46 % for improving water quality

Hydrogen gas production from glycerol via steam reforming using nickel loaded zeolite catalyst

Glycerol is the main by-product of biodiesel production that produces from transesterification process. In this research, focused was on hydrogen production via glycerol steam reforming using nickel loaded HZSM-5 catalyst. The catalysts were prepared by using different loading amount of nickel (0.5, 1.0, 5.0, 10.0 and 15 wt %) on HZSM-5 catalyst through the wet impregnation method at temperature 500 ºC and atmospheric pressure. The catalyst was characterized by using XRD, FTIR and SEM. Then, only 15 wt % Ni loading has been chosen based on the parameter which is different range of catalyst weight (0.3-0.5g) at different range of glycerol flow rate (0.2-0.4mL/min) at temperature 600 ºC and atmospheric pressure. The products were analyzed by using gas-chromatography with thermal conductivity detector (GC-TCD) where it is used to identify the yield of hydrogen. The data of the experiment were analyzed by using Response Surface Methodology (RSM) in order to study the relationship of catalyst weight and glycerol flow rate. The results showed that the optimum condition to produce a maximum hydrogen yield with 15wt% Ni/HZSM-5 catalyst was 78.10004% at glycerol flow rate of 0.356484 mL/min and catalyst weight of 0.429267 g