Mangrove litter production and seasonality of dominant species in Zanzibar, Tanzania

This study is aimed at examining the litter production and seasonality of Avicennia marina, Bruguiera gymnorhiza, and Rhizophora mucronata. Litter was collected using nylon litter traps of 1 mm2 mesh size in the Uzi-Nyeke mixed mangroves, Zanzibar, over a period of 2 years. Contents were sorted, dried, weighed, and the average daily litter production for each component was calculated. A distinct seasonality and species variation were found in all mangrove litter components. The average annual litterfall rate was higher in B. gymnorhiza, followed by R. mucronata and A. marina (3.0, 2.8, and 2.0 ton dry wt. ha-1year–1 respectively). Leaf fraction was the main component of litter in all species, but fruit and flower for R. mucronata also had a considerable contribution to the total litterfall. The presented patterns of litter production are associated with average temperature and wind speed which are both strongly correlated with litter seasonality. Our data contributes to the body of knowledge on  patterns of litter production and the ecological integrity of mangrove forests in Zanzibar.Keywords: Litterfall, mangrove species, seasonal pattern

Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency–inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks

On the Integration of Similarity Measures with Machine Learning Models to Enhance Text Classification Performance

Several techniques have long been proposed to enhance text classification performance, such as: classifier ensembles, feature selection, the integration of similarity measures with classifiers, and meta-heuristic algorithms. The integration of similarity measures with machine learning models (ML), however, has not yet received thorough analysis for text classification. As a result, in an effort to thoroughly investigate the impact of similarity measures integration with ML models, this work makes three major contributions: (1) proposing newly-integrated models and presenting benchmarking studies for integration methodology over balanced/imbalanced datasets; (2) offering detailed analysis for dozens of integrated models that are established, and experimentally proven, to significantly outperform state-of-the-art performance. The models\u27 construction used fourteen similarity measures, three knowledge representations (BoW, TFIDF, and Word embedding), and five models (Support Vector Machine, N-Centroid-based Classifier, Multinomial Naïve Bayesian, Convolutional Neural Network, and Artificial Neural Network); and (3) introducing significantly-effective and highly-efficient variations of these five models. The evaluation study has been conducted internally for integrated models against their baselines, and externally against the state-of-the-art models. While the internal evaluation constantly showed a total enhancement rate of 49.3% and 59% over the balanced and imbalanced datasets, respectively, the external evaluation attested to the superiority of the integrated models

A set theory based similarity measure for text clustering and classification

© 2020, The Author(s). Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency

Towards Highly-Efficient k-Nearest Neighbor Algorithm for Big Data Classification

the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a test point in a feature space. A large number of works have been developed in the literature to accelerate the speed of data classification using kNN. In parallel with these works, we present a novel K-nearest neighbor variation with neighboring calculation property, called NCP-kNN. NCP-kNN comes to solve the search complexity of kNN as well as the issue of high-dimensional classification. In fact, these two problems cause an exponentially increasing level of complexity, particularly with big datasets and multiple k values. In NCP-kNN, every test point’s distance is checked with only a limited number of training points instead of the entire dataset. Experimental results on six small datasets, show that the performance of NCP-kNN is equivalent to that of standard kNN on small and big datasets, with NCP-kNN being highly efficient. Furthermore, surprisingly, results on big datasets demonstrate that NCP-kNN is not just faster than standard kNN but also significantly superior. The findings, on the whole, show that NCP-kNN is a promising technique as a highly-efficient kNN variation for big data classification

The Impact of Financial Risks on the Firms’ Performance

Firms are exposed to a variety of risks including credit risk, liquidity risk, foreign exchange risk, market risk and interest rate risk. An efficient risk management system is needed in time in order to control these risks. Managing risk is one of the basic tasks to be done, once it has been identified and known. The risk and return are directly related to each other, which means that increasing one will subsequently increase the other and vice versa. Financial risks have a great impact on firm’s performance. The study also assessed the current risk management practices of the firms and linked them with the firms’ financial performance. The findings confirm whether financial risks can be contained or managed in order for firms to achieve profit maximization for its shareholders. Keywords: Financial Risk; Firm’s Performance; Interest rate parity; Liquidity gap; Liquidity risk; Risk Management

A General Framework of Particle Swarm Optimization

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, we propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence

On hierarchical clustering-based approach for RDDBS design

Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to develop effective solutions for DDBS design problems. But the great majority of them barely consider the RDDBS\u27s initial design. Therefore, this work aims at proposing a clustering-based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design. To ensure that each operation flows into the next without any increase in complexity, fragmentation and allocation are done simultaneously. With this approach, the main goals are to minimize communication expenses, response time, and irrelevant data access. Most importantly, it has been observed that the proposed approach may effectively expand RDDBS performance by simultaneously fragmenting and assigning various relations. Through simulations and experiments on synthetic and real databases, we demonstrate the viability of our strategy and how it considerably lowers communication costs for typical access patterns at both the early and late stages of design

Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures

Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model\u27s complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users\u27 history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system’s performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top state-of-the-art performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model

Therapeutic effect of antimyeloma antibodies conjugated with gold nanoparticles on the growth of myeloma cell line

Nanobiotechnology is a field of biomedical application of nanosize system which is a rapidly developing area within nanotechnology among these nonmaterial, gold nanoparticles (AuNPs) which receive a significant attention due to their unique physical, chemical and biological properties. AuNPs and bio-conjugated AuNPs have been widely used in cancer treatment besides other application on cancer detection and diagnosis. In this study the potential therapeutic effects of polyclonal Antimyeloma antibody (AbMM) conjugated to AuNPs in comparison with naked (AbMM) or AuNPs alone in modulation of proliferation capacity in vitro and different stages of MM cell cycle have been evaluated besides evaluation of their effects on tumor growth delay. Effect of AuNPs , (AbMM) and (Nanogold -Antimyeloma Antibodies conjugate) (gold-AbMM) on growth of myeloma cells showed decreasing in multiple myeloma SP2OR (MM) viability with increasing dose of these treatments compared to that of control also a significant enhancement in the apoptosis after conjugation of Nanogold to the Antimyeloma was observed. The induction of apoptosis with gold-AbMM was significantly higher than the MM cells exposed to only AbMM or AuNPs. The study concluded that the efficacy of (gold-AbMM) on induced myeloma growth inhibition is better than that of individual AuNPs and AbMM