The pollution of rivers and lakes is of concern because of the threat to public water supplied and because of the damage caused to natural ecosystems. Waterborne diseases remain rife in the Third World, while in developed countries there has been a decline in the quality of water. It has also become apparent that pollutants may destroy ecosystems hundreds of miles from their source. Some of these toxic materials such as lead and PCBs also pose threats to public health. This text describes the sources of various types of pollutants, their effects on aquatic organisms, ecosystems and man, using case histories where appropriate. Ways of mitigating the effects of pollutants by control and ecosystem manipulation are described. Techniques for assessing the effects of pollution, both in field and laboratory, are introduced. The role of the biologist in pollution assessment and control and in the wider sphere of mangement of acquatic resources is also examined.

Biology of Freshwater Pollution.

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Handbook Of Biometrics

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort

Improved salp swarm algorithm based on particle swarm optimization for feature selection

Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.

https://dl.acm.org/doi/pdf/10.1145/3274658

A Perspective Analysis of Handwritten Signature Technology

The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores.

/pdf/facial-soft-biometrics-for-recognition-in-the-wild-recent-4nqk6152f1.pdf

Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

/pdf/online-handwritten-signature-verification-using-neural-4ip7o26f8h.pdf

Online handwritten signature verification using neural network classifier based on principal component analysis.

Face as a biometric attribute has been extensively studied over the past few decades. Even though, satisfactory results are already achieved in controlled environments, the practicality of face recognition in realistic scenarios is still limited by several challenges, such as, expression, pose, occlusion, etc. Recently, the research direction is concentrating on the prospects of complementing face recognition systems with facial soft biometric traits. The ease of extracting facial soft biometrics under several varying conditions has mainly resulted in the ability of using the traits to, either improve the performance of traditional face recognition systems, or performing recognition solely based on many facial soft biometrics. This paper presents state-of-the-art techniques in facial soft biometrics research by describing the type of traits, feature extraction methods, and the application domains. It indicates the most recent and valuable results attained, while also highlighting some possible future scientific research directions to be investigated.

Recent advances in facial soft biometrics

Integration of multiple soft biometrics for human identification

This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.

/pdf/assessment-of-predictive-models-for-chlorophyll-a-3lkrffadr6.pdf

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

This poster presents a database of Malaysian signatures named SIGMA describing the strategies adopted in compiling it and its technical details. The database consists of over 6,000 genuine signature samples and over 2,000 forged signature samples of Malaysian nationals. The signature samples are collected simultaneously in both online and offline mode thereby eliminating the variation which is inevitable if the two modes are done at different times. This is particularly appealing to studies which are aimed at exploring the co-relationships between static signature features and their dynamic counterparts (that are derived from offline and online signature samples respectively). Furthermore, all forgeries in the database are 'skilledforgeries'. The collection of only Malaysian signatures makes the database very useful for designing a signature verification system that is optimized to verify Malaysian signatures.

Sharifah Mumtazah Syed Ahmad

Papers

Online handwritten signature verification using neural network classifier based on principal component analysis.

Recent advances in facial soft biometrics

Integration of multiple soft biometrics for human identification

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

SIGMA - A Malaysian signatures’ database