Showing papers by "University Institute of Technology, Burdwan University published in 2020"
TL;DR: It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptibles-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network on X-ray and CT images.
Abstract: In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.
198 citations
170 citations
135 citations
TL;DR: In this paper, the feasibility of integrating PV/wind power systems into existing unreliable grid/diesel generator systems for supplying the critical loads of industrial parks in three different regions of Ethiopia is examined.
85 citations
TL;DR: In this work, a new compartmental mathematical model of COVID-19 pandemic has been proposed incorporating imperfect quarantine and disrespectful behavior of the citizens towards lockdown policies, which are evident in most of the developing countries.
Abstract: In this work, a new compartmental mathematical model of COVID-19 pandemic has been proposed incorporating imperfect quarantine and disrespectful behavior of citizens towards lockdown policies, which are evident in most of the developing countries. An integer derivative model has been proposed initially and then the formula for calculating basic reproductive number, R 0 of the model has been presented. Cameroon has been considered as a representative for the developing countries and the epidemic threshold, R 0 has been estimated to be ~ 3.41 ( 95 % CI : 2.2 − 4.4 ) as of July 9, 2020. Using real data compiled by the Cameroonian government, model calibration has been performed through an optimization algorithm based on renowned trust-region-reflective (TRR) algorithm. Based on our projection results, the probable peak date is estimated to be on August 1, 2020 with approximately 1073 ( 95 % CI : 714 − 1654 ) daily confirmed cases. The tally of cumulative infected cases could reach ~ 20, 100 ( 95 % CI : 17 , 343 − 24 , 584 ) cases by the end of August 2020. Later, global sensitivity analysis has been applied to quantify the most dominating model mechanisms that significantly affect the progression dynamics of COVID-19. Importantly, Caputo derivative concept has been performed to formulate a fractional model to gain a deeper insight into the probable peak dates and sizes in Cameroon. By showing the existence and uniqueness of solutions, a numerical scheme has been constructed using the Adams-Bashforth-Moulton method. Numerical simulations have enlightened the fact that if the fractional order α is close to unity, then the solutions will converge to the integer model solutions, and the decrease of the fractional-order parameter (0
80 citations
TL;DR: This work proposes and implements an integrated convolutional mixture density recurrent neural network that significantly outperforms the competitive models for predicting the drug-drug interaction score.
Abstract: A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.
74 citations
TL;DR: The Border Collie’s unique herding style from the front as well as from the sides is adopted successfully in this paper and provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm.
Abstract: In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.
47 citations
TL;DR: Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information and focuses on a structured or unstructure approach to data processing.
Abstract: Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information. Where textual data processing focuses on a structured or unstructure...
45 citations
TL;DR: In this article, the authors analyzed and compared 10 numerical methods, namely, the empirical method of Justus (EMJ), the empirical Method of Lysen (EML), the method of moments (MoM), the graphical method (GM), the Mabchour's method (MMab), the energy pattern factor method (EPFM), the maximum likelihood method (MLM), modified maximum likelihood (MMLM) and the equivalent energy method (EEM) in order to estimate wind energy potential.
27 citations
TL;DR: In this article, the authors evaluated the hydrochemistry of shallow groundwater and its suitability for drinking and irrigation purposes in Tarmiah district, Baghdad governorate, Iraq and found that the results have shown that the quarter of the samples were suitable for both studied purposes.
27 citations
TL;DR: In this article, the authors evaluated hydrological performance of multiple globally available precipitation products in the data scarce region of the upper Blue Nile basin over multi-scales (1656-199,812 km2) focusing on multi-year (2000-2012) for daily simulation.
TL;DR: In this article, a novel route by which carbon nanomaterials (CNMs) might be inserted into the structure of g-C3N4 to form hybrid materials that possess great surface area with high photocatalytic and adsorption properties was investigated.
Abstract: To overcome the drawbacks of pure g-C3N4 in photocatalysis, namely insufficient light absorption and fast recombination of its photogenerated charges, g-C3N4-based nanocomposites were investigated. In detail, this study focuses on a novel route by which carbon nanomaterials (CNMs) might be inserted into the structure of g-C3N4 to form hybrid materials that possess great surface area with high photocatalytic and adsorption properties. The g-C3N4/CNMs composites with high photo-activity were obtained via a one-pot synthesis using urea and green tea leaves as cheap and renewable materials. After exfoliation, porous nanosheets with a surface area of about 128 m2 g−1, providing more active sites for both adsorption and efficient photocatalytic degradation of methylene blue, were obtained. The green tea leaves seem to act as a templating agent that control the morphology, growth, and final structure of the composite. From the first-order kinetic model, the reaction rate constant for the nanosheets g-C3N4/CNMs was determined to be kapp =4.7 min−1, which was approximately more than twice that of pure nanosheets g-C3N4 (kapp = 2.0 min−1). Besides, the g-C3N4/CNMs nanocomposite shows an accurate reusability and stability as compared to g-C3N4.
TL;DR: The proposed landslide detection technique was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography and showed that the proposed method can be used for landslide inventory mapping and risk assessments.
Abstract: This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments.
TL;DR: In this paper, the authors reviewed the advancements, challenges, and opportunities regarding improving irrigation water use efficiency in Ethiopia and revealed the challenges that hinders water usage efficiency improvement including crop diseases, socioeconomic factors, institution and policy-related issues, limitation in technical and human capacity, lack of agricultural input as well as market and nature-related factors.
TL;DR: A Fog Computing based Hybrid Deep-Learning Framework (FC-HDLF) that can find possible defective products and a decision-making framework for multi-agents is built to ensure a production process architecture to optimize production processes.
TL;DR: In this article, the phase change materials (PCM) using metal foam structures for storing solar energy in form of thermal heat is presented. And the thermal efficiency of the various PCM-foam composite thermal energy storage tanks ranged from 60-70 % and 80-85 % on using water and air as the heat extraction media.
Abstract: The present work consists of increasing the thermal conductivity of the phase change materials (PCM) using metal foam structures for storing solar energy in form of thermal heat. In this regard, copper foam and copper/aluminium wire woven foam structures have been used to increase the thermal conductivity for a faster heat storage and extraction of heat from the PCM. The latent heat thermal energy storage (LHTES) systems with capacity of storing 300 KJ of thermal energy have been designed using the PCM and metal foam structures. Both the PCM–aluminium wire woven foam and PCM-copper foam composites took similar time for melting of PCM. The heat extraction of PCM–aluminium wire woven foam was high and found to be comparable to the PCM–copper foam composite system. The thermal efficiency of the various PCM-foam composite thermal energy storage tanks ranged from 60–70 % and 80–85 % on using water and air as the heat extraction media. The heat extraction continued for 3 – 4 hrs in case of cold water and 7 – 8 hrs in the case of cold air. The temperature of the cold air/water increased to 2 – 2.5 times of the original temperature after passing through the LHTES.
TL;DR: In this paper, the performance of two photonic crystal structures composed of Si/SiO2 and TiO2/Si O2 are optimized and compared for potential use as thermophotovoltaic (TPV) optical filters.
TL;DR: In this article, a polyaniline-graphene quantum dot (PANI-GQD) hybrid was synthesized using the chemical in situ polymerization method and the synthesized materials were characterized using UV-visible (UV-Vis) spectrograms, photoluminescence (PL) spectroscopy, currentvoltage (I-V) characteristic, thermal gravimetric analysis (TGA), Raman spectrum, and X-ray diffraction.
Abstract: Polyaniline–graphene quantum dots (PANI–GQDs) are considered as an important candidate for applications in photovoltaic cells. In this work, GQDs were prepared using sono-Fenton reagent from reduced graphene oxide (rGO). PANI–GQD hybrid was also synthesized using the chemical in situ polymerization method. The synthesized materials were characterized using UV–visible (UV–Vis) spectroscopy, photoluminescence (PL) spectroscopy, current–voltage (I–V) characteristic, thermal gravimetric analysis (TGA), Raman spectroscopy, and X-ray diffraction (XRD). Dynamic light scattering was also used to estimate the lateral size of GQDs. The enhanced visible-light absorbance in the hybrid was confirmed by UV–Vis analysis and the decrease in intensity around 3461 cm−1 in FT-IR spectra was due to the interaction between functional groups of PANI with GQDs. This led to improved thermal stability and conductivity as observed from TGA and I–V analysis, respectively. Moreover, the Raman spectrum for PANI–GQDs showed a decrease in the peak at ~ 1348 and ~ 1572 cm−1 as compared to PANI and GQDs. Similarly, from the XRD profile of PANI–GQDs, a shift in peak was observed due to an alteration in the microstructure. A sandwich device with cell structure glass/ITO/PANI–GQDs/Al was fabricated and its application was tested. Current density–voltage (J–V) curve of the device was measured with a Keithley SMU 2400 unit under an illumination intensity of 100 Wm−2 simulating the AM 1.5 solar spectrum. The hybrid exhibited photovoltaic properties, and 0.857% efficiency was observed in response to the applied voltage. This work suggests that PANI can be used as an alternative material for photovoltaic cells.
TL;DR: Flying Ad-hoc Networks (FANETs) and Unmanned Aerial Vehicles (UAVs) are widely utilized in various rescues, disaster management and military operations nowadays.
Abstract: Flying Ad-hoc Networks (FANETs) and Unmanned Aerial Vehicles (UAVs) are widely utilized in various rescues, disaster management and military operations nowadays. The limited battery power and high ...
TL;DR: In this article, a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks is presented, and decision tree outperforms all other models with an accuracy of 97.65%.
TL;DR: In this article, the influence of moderator content on the microstructure of Al/TiC composites was investigated and the highest value of the relative increase of yield strength was found in composite with 50% of moderator due to relatively small TiC particle size, well separated and uniformly distributed in the matrix.
Abstract: Al matrix nanocomposites reinforced with TiC particles have been successfully fabricated by self-propagating high-temperature reaction during casting from aluminium 1000 alloy and a mixture of powders of Ti, C and Al used as a moderator. The most important issue was to estimate the influence of moderator content on the microstructure. The microstructure of composites containing a lower amount of moderator (1% and 10%) consists of TiC particles, segregated mainly at grain boundaries of aluminium matrix, while a higher moderator content (>50%) allows to obtain materials with nearly homogenous distribution of TiC particles. Moreover, the elimination of brittle, additional phases e.g. TiAl3 and Al4C3, commonly observed in the Al/TiC composites, was evidenced. However, still small amounts of Ti3AlC and TiAl2.4Si0.6 phases that reduce the capacity of TiC were identified. The composites exhibit improved mechanical properties with regard to aluminium base alloy. The highest value of the relative increase of yield strength was found in the composite with 50% of moderator due to relatively small TiC particle size, well separated and uniformly distributed in the matrix.
TL;DR: The hemispheric asymmetry of the solar-flare index during 1976-2018 from the Kandilli Observatory is studied in this paper, where different methodologies, such as cross-correlation analysis, rescaled-range analysis, empirical mode decomposition, and date-compensated discrete Fourier transform, have been used on the hemispheres of the index and absolute asymmetry data to study various inherent characteristics.
Abstract: The hemispheric asymmetry of the solar-flare index during 1976 – 2018 from the Kandilli Observatory is studied in this investigation. The temporal duration covers Solar Cycles 21 – 23 and almost the whole of Solar Cycle 24. Different methodologies, such as cross-correlation analysis, rescaled-range analysis, empirical mode decomposition, and date-compensated discrete Fourier transform, have been used on the hemispheric solar-flare index as well as on absolute asymmetry data to study various inherent characteristics. We observed that: i) the temporal characteristics in the northern and southern hemispheres are different during the progression of a solar cycle; ii) the T-test indicates that Solar Cycles 21 and 23 do not have any dominant hemispheric effect, whereas Solar Cycle 22 and 24 have South-dominated hemispheric characteristics; iii) the southern hemisphere is leading by ten, three, and one months during Solar Cycles 21, 22, and 24, respectively, and for Solar Cycle 23 the hemispheres are in phase; iv) anti-persistence as well as short memory-dependent characteristics are present in both the hemispheric solar-flare index and the absolute asymmetry data; v) all of the time-series data have well-known periods of 11 years and 27 days as well as short-term periods around 7 days and 14 days. Apart from those, several mid-term periodicities such as the Rieger periodicity and quasi-biennial oscillations (QBOs) are also found in both hemispheric solar-flare index as well as absolute asymmetry index data; vi) the Waldmeier effect is also validated using solar-flare-index data. These results will enrich our knowledge about the distribution of hemispheric asymmetry in solar-flare-index data and may reveal some valuable points about asymmetry behaviors.
TL;DR: In this article, the runoff product from two multi-model global water resources reanalyses (WRRs), available at 0.5° and 0.25° grid resolutions, were evaluated in the upper Blue Nile basin.
Abstract: The increasing availability of global observation datasets, both from in situ and remote sensors, and advancements in earth system models and data assimilation algorithms have generated a number of water resources reanalysis products that are available at global scale and high spatial and temporal resolutions. These products hold great potential for water resources applications, but their levels of uncertainty need to be evaluated at local scale. In this work, we evaluate the runoff product from two multi-model global water resources reanalyses (WRRs), available at 0.5° (WRR1) and 0.25° (WRR2) grid resolutions, which were produced within the framework of a European Union project (eartH2Observe) in the upper Blue Nile basin. Analysis indicates that the recently released WRR2 UniK product exhibits consistently better performance statistics than the earlier coarser-resolution WRR1 and the rest of the WRR2 products at all ranges of temporal and spatial scale evaluated. Streamflow simulations based on gauged rainfall forcing and the locally set hydrological model CREST outperforms all the other products, including UniK. Global hydrological products can be a data source for various water resources planning and management applications in data-scarce areas of Africa. This study cautions against using available global hydrological products without prior uncertainty evaluation.
TL;DR: In this paper, an artificial neural network (ANN) model was coupled with a phenomenological one to describe the heat and mass transfer during the drying of these materials, specifically of guava pieces, in a spouted bed dryer.
TL;DR: In this article, the authors used geotechnical, mechanical, thermal, and durability laboratory tests on adobe bricks formulated from the soil with 0, 1, 2, 3, and 4 % of millet waste.
01 Jun 2020
TL;DR: Given the amount of atrazine translocated into the edible leaves, this herbicide should not be used for the cultivation of the two vegetables studied, contrary to what is practiced in the field in Ngaoundere (Cameroon).
Abstract: The objective of this work is to assess the level of contamination of the soil-plant system by atrazine which is a pesticide classified as dangerous in the European Union, but approved in Cameroon. To achieve this aim, two plant species (Amaranthus hybridus and Corchorus olitorius) were grown in pots according to the method developed by farmers in the field. In other words, each pot was treated with three dosages of atrazine (22.5 g/L; 45 g/L and 90 g/L). Later, the crops were harvested at four stages of growth and the atrazine was analysed in the soil and in the plants by HPLC-UV. The results show that the concentration of atrazine decreased from 4.66 mg/L in the soil after 15 days of growth of the plant to 1.96 mg/L after 2 months of growth. This evolution is also observed within the two plant species with a significant treatment effect. Thus, the highest concentrations of atrazine were found in the roots of plants harvested from soils treated with 90 g/L of atrazine. Besides, the maximum average content of atrazine of 0.74 mg/kg of dry matter was found in the edible part of the two plant species at their stage of maturity. Finally, atrazine is persistent in the agricultural soil after two months regardless of the dose. Given the amount of atrazine translocated into the edible leaves, this herbicide should not be used for the cultivation of the two vegetables studied, contrary to what is practiced in the field in Ngaoundere (Cameroon).
01 Feb 2020
TL;DR: Information on the latest techniques used to assist visually impaired people to assist them is provided, including detailed description of tools and techniques which include summary, various merits and demerits followed by the future challenges.
Abstract: One of the major effect of blindness is the loss of ability to travel from one place to another safely. Visually Impaired People came across various problems in their daily life which make them anxious to do their basic works and to deal with surroundings. This paper provides information on the latest techniques used to assist visually impaired people to assist them. It provides detailed description of tools and techniques which include summary, various merits and demerits followed by the future challenges. This information will aid researchers to develop more efficient solutions for assisting visually impaired persons.
TL;DR: This paper has compared performance of BPMOGA based classifier with fourteen GA and non-GA based classifiers and Statistical test shows that the performance of the proposed classifier is either superior or comparable to other classifiers.
Abstract: This paper presents a novel Bi-Phased Multi-Objective Genetic Algorithm (BPMOGA) based classification method. It is a Learning Classifier System (LCS) designed for supervised learning tasks. Here we have used Genetic Algorithms (GAs) to discover optimal classifiers from data sets. The objective of the work is to find out a classifier or Complete Rule (CR) which comprises of several Class Specific Rules (CSRs). Phase-I of BPMOGA extracts optimized CSRs in I F − T H E N form by following Michigan approach, without considering interaction among the rules. Phase-II of BPMOGA builds optimized CRs from CSRs by following Pittsburgh way. It combines the advantages of both approaches. Extracted CRs help to build CSRs for the next run of phase-I. Hence, phase-I and phase-II are cyclically related, which is one of the uniqueness of BPMOGA. With the help of twenty one benchmark data sets from the University of California at Irvine (UCI) machine learning repository we have compared performance of BPMOGA based classifier with fourteen GA and non-GA based classifiers. Statistical test shows that the performance of the proposed classifier is either superior or comparable to other classifiers.
TL;DR: In this paper, the authors identify the most usable surface of water bodies in Amhara regional, state irrigation dams for generating electrical power and find the majority of the usable areas were found in the middle of the water surface.
Abstract: The majority of the Ethiopian population lives in rural areas and uses wood for domestic energy consumption. Using wood and fuel for domestic uses accounts for deforestation and health problems, which is also dangerous for the environment. The Ethiopian government has been planning to generate power from available renewable resources around the community. Therefore, determining the water surface potential of energy harvesting with floating solar photovoltaic system by using geographic information system is used to support decision-makers to use high potential areas. To identify useable areas for floating solar photovoltaic, factors that affect the usability were identified and weighted by using Analytical Hierarchy Processes. Thus, weighted values and reclassified values were multiplied to do the final usability map of floating solar photovoltaic with ArcGIS software. Due to the improper location of floating solar photovoltaic, efficiency is dropped. Therefore, the objective of this study was to identify the most usable surface of water bodies in Amhara regional, state irrigation dams for generating electrical power. The usability of the water surface for floating solar photovoltaic power plant was 63.83%, 61.09%, and 57.20% of Angereb, Rib, and Koga irrigation dams, respectively. The majority of the usable areas were found in the middle of the water surface. Nature water surface is a key factor in generating solar energy; it affects the floating solar photovoltaic and irradiance coming to the solar photovoltaic panel surface.
TL;DR: The preparedness of undergraduate students at six dental institutions in Malaysia was comparable to students from developed countries and the dental undergraduate preparedness assessment scale is a useful tool and dental institutions may be used for self-assessment as well as to obtain feedback from the supervisors.
Abstract: Aims: To evaluate the self-perceived preparedness of final-year dental undergraduate students in dental public universities in Malaysia.
Methods: Final-year dental undergraduate students from six dental public universities in Malaysia were invited to participate in an online study using a validated Dental
Undergraduates Preparedness Assessment Scale DU-PAS.
Results: In total, about 245 students responded to the online questionnaire yielding a response rate of 83.05%. The age range of the respondents was 23-29 years with a mean age of 24.36 (SD 0.797). The total score obtained by the respondents was ranged from 48 to 100 with a mean score of 79.56 (SD 13.495). Weaknesses were reported in several clinical skills, cognitive and behavioural attributes.
Conclusions: The preparedness of undergraduate students at six dental institutions in Malaysia was comparable to students from developed countries. The dental undergraduate preparedness assessment scale is a useful tool, and dental institutions may be used for self-assessment as well as to obtain feedback from the supervisors.