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This paper aims to bring in augmented reality (AR) into navigation systems to rectify the issues mentioned. This paper proposes an AR enhanced navigation system for location automated teller machine (ATM) counters (AR-ATM) and branches of banks based on user’s choice. Upon selecting the ATM, the navigational path to the destination is drawn from the current location, thereby the user can reach the ATM through the optimal path.

Traditional navigation systems require users to map with the real world environment as and when required and also may lead to incorrect path due to minor difference in distance. The traditional navigation systems’ also does not take into consideration the ergonomics and safety of the user.

In this system, a camera lens is used, which is directed down the street at eye level and the application displays the location of ATMs and bank branches and also provides information about the locations like distance and time through the AR superimposed object.

The application also provides indoor navigation, especially in a multi-storeyed building. Experiments are performed on smartphones that support AR, and the results are promising with no lag in time frame of the real object and virtual object. To determine the factors that regulate the suggested AR tracking mechanism, a quantitative evaluation of the experimental data is also performed. The testing of implemented AR-ATM from the standpoint of end-users is undertaken to evaluate real-time usage comfortability, and the results have been determined to be extremely satisfactory.

The purpose of this study is to apply Buongiorno’s two phase model to analyse double diffusion natural convection in a square enclosure filled with nanofluids.

A computational code based on the SIMPLE algorithm and finite volume method is used to solve the non-dimensional governing equations.

The nanoparticle plays a crucial role when thermal and solutal buoyancy forces are equal and opposing.

This is the first paper to apply Buongiorno’s two phase model for double diffusion natural convection in enclosures filled with nanofluids.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

This paper intent to design, develop, and fabricate a robust cascaded controller based on the dual loop concept i.e. Fuzzy Sliding Mode concept in the inner loop and traditional Proportional Integral controller in the outer loop to reduce the unknown dynamics and disturbances that occur in the DC-DC Converter.

The proposed Fuzzy sliding mode approach combines the merits of both SMC and Fuzzy logic control. FSMC approach reduces the chattering phenomena that commonly occurs in the sliding mode control and speed up the response of the controller.

In most of the research work, the inner current loop of cascaded controller was designed by sliding mode control. In this paper FSMC is proposed and its efficacy is confirmed with SMC -PI. In most uncertainties, FSMC-PI produces null maximum peak overshoot and a very less settling time of 0.0005 sec.

The presence of Fuzzy SMC in the inner loop ensure satisfactory response against all uncertainties such as steady state, circuit parameter variations and sudden line and load disturbances.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

Population growth has remained a key issue facing developing economies in the world. While developed countries are experiencing diminished or negative population growth, many countries in sub-Saharan Africa including Nigeria are having population growth above the economic growth rate. With the deadline for the sustainable development goals approaching, attention is increasingly being focused on population growth and human capital development. Extant literature focused on population growth, human resource utilization and economic growth but this study aims to examine the effect of population growth on human resource utilization.

Using secondary data for the period 1990-2018, the study conducted unit root test and co-integration analyses to determine the stationarity and correlation in the long-run in the variables. The study used the error correction model to ascertain the speed at which shocks can be corrected in the long-run. Granger causality test was also carried out to ascertain the direction of causality among the variables.

The empirical results revealed that population growth has a negative and significant effect on human resource utilization. The study also revealed that unidirectional causality runs from employment rate to population growth rate and a unidirectional causality runs from employment growth rate to expected years of schooling. The Nigerian Government needs to not only control population growth but also focus on the quality of education.

The paper provides insights into the relationship between population growth and human capital utilization in Nigeria focusing on the 1986-2018 period.

With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances.

The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome.

Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally.

The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.

The study aims to describe the main classes of antioxidants existing in fruit, beverages, vegetables and herbs and the different extraction and application of antioxidants in food. Oxidative degradation of lipids, especially induced by reactive oxygen species, leads to quality deterioration of foods and cosmetics and could have harmful effects on health. A major challenge is to develop tools to assess the antioxidant capacity and real efficacy of these molecules. Recently, many review papers regarding antioxidants from different sources and different extraction and quantification procedures have been published. However, none of them has all the information regarding antioxidants (sources, extraction and application in food).

This paper tries to take a different perspective on antioxidants for the new researcher involved in this field.

Antioxidants from fruit, vegetables and beverages play an important role in human health, for example, preventing cancer and cardiovascular diseases and lowering the incidence of different diseases. A number of plant products act as scavengers of free radical species and so have been classified as antioxidants. Antioxidants are an important group of food additives that have the ability to protect against detrimental change of oxidizable nutrients and consequently they extend shelf-life of foods.

Most of the antioxidants present in foods are phenolic and polyphenolic compounds, but their efficacy in food for the prevention of oxidation or in the body for dealing with oxidative stress and its consequences depends on different factors.

This study collected the last finding in the field of sources and applications of natural antioxidants.

This paper aims to evaluate the bioremediation [chemical oxygen demand (COD) and color removal] of the effluent from the cardboard recycling industry in Yazd, central province of Iran, using mixed fungal culture.

First, the effluent samples from the cardboard recycling industry were cultured on potato dextrose agar medium to isolate native fungal colonies. The grown colonies were then identified using morphological macroscopic and microscopic characteristics to choose the dominant fungi for bioremediations. The mixed cultures of Aspergillus niger, Aspergillus flavus and Penicillium digitatum were finally used for bioremediation experiments of the cardboard recycling industry. A suspension containing 1 × 10⁶ CFU/ml of fungal spores was prepared from each fungus, separately and their homogenous mixture. Sewage samples were prepared and sterilized and used at 25%, 50% and 90% dilutions and pH levels of 5, 7 and 8 for bioremediation tests using mixed fungal spores. Following that, 10 ml of the mixed fungal spores were inoculated into the samples for decolorization and COD removal and incubated for 10 days at 30°C. The amount of COD removal and decolorization were measured before incubation and after 3, 6 and 10 days of inoculation. In this research, the color was measured by American Dye Manufacturer Institute and COD by the closed reflux method. The results of the present study were analyzed using SPSS 21 statistical software and one-way ANOVA tests at p-value < 0.05.

The results of this research showed that the mean decolorization by mixed fungal culture over 10 days at pH levels of 5, 7 and 8 were 44.40%, 45.00% and 36.84%, respectively, and the mean COD removal efficiency was 71.59%, 73.54% and 16.55%, respectively. Moreover, the mean decolorization at dilutions of 25%, 50% and 90% were 45.00%, 31.93% and 30.53%, respectively, and the mean COD removal efficiency was 73.54%, 62.38% and 34.93%, respectively. Therefore, the maximal COD removal and decolorization efficiency was obtained at dilution of 25% and pH 7.

Given that limited studies have been conducted on bioremediation of the effluent from the cardboard recycling industry using fungal species, this research could provide useful information on the physicochemical properties of the effluent in this industry.

The deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs).

RSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.

The results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets.

The algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.