Recent Submissions

  • Transforming product labels using digital technologies to enable enhanced traceability and management of hazardous chemicals

    Takhar, Sukhraj; Liyanage, Kapila; University of Derby (Inderscience, 2021-06-08)
    Manufacturers that produce, distribute or market physical products are likely to be impacted by numerous chemical and product regulations. Manufacturers must identify chemical substances which appear within mixtures, materials, formulations, raw materials, components, assemblies and finished products. This results in a very manual and resource intensive process of collection of chemical substances in products data, where definitions arise from internal, industry standards, supplier and customer requirements and often sourced from multiple supply chain actors. This paper contributes to existing literature by identifying a research gap in transforming current manual state data collection tasks via the utilisation of digital technologies, leveraging real-time data collection using smart labels to identify chemicals contained within products. The proposed design enables manufacturers to identify the use of chemicals consumed in a automated manner and enabling appropriate risks to be identified and managed accordingly. The design can be further expanded in the proposed collaborative data sharing network.
  • Realignment of Product Stewardship towards Chemical Regulations, the Circular Economy and Corporate Social Responsibility – a Delphi Study

    Liyanage, Kapila; Takhar, Sukhraj; University of Derby (Sepuluh Nopember Institute of Technology (ITS), 2021-07)
    Chemical regulations exist to limit and control the amount of hazardous chemical substances being used by industry. Increasing awareness of diminishing natural resources, increasing pollution, and reducing the amounts of harmful waste, has led towards increasing societal and regulatory pressure on industry to change from the traditional closed-loop manufacturing towards the adoption of sustainable materials and open-loop manufacturing systems as part of the Circular Economy. Corporate Social Responsibility (CSR) extends the relationship between industry and society. Product Stewardship (PS) provides a platform for organizations to assess impacts to manufacturing systems ensuring adequate measures are in place to understand, control or limit any impact(s) from manufacturing and using products. The research question answered in this paper relates to understanding the impacts on PS. This paper has been written based on a literature review and Delphi study. The outcomes from this paper will attempt to outline a framework for PS to align with Chemical Regulations, the Circular Economy and CSR.
  • On Generalized Lucas Pseudoprimality of Level k

    Andrica, Dorin; Bagdasar, Ovidiu; Babeş-Bolyai University, 400084 Cluj-Napoca, Romania; University of Derby (MDPI AG, 2021-04-12)
    We investigate the Fibonacci pseudoprimes of level k, and we disprove a statement concerning the relationship between the sets of different levels, and also discuss a counterpart of this result for the Lucas pseudoprimes of level k. We then use some recent arithmetic properties of the generalized Lucas, and generalized Pell–Lucas sequences, to define some new types of pseudoprimes of levels k+ and k− and parameter a. For these novel pseudoprime sequences we investigate some basic properties and calculate numerous associated integer sequences which we have added to the Online Encyclopedia of Integer Sequences.
  • On k-partitions of multisets with equal sums

    Andrica, Dorin; Bagdasar, Ovidiu; Babeş-Bolyai University of Cluj-Napoca, Cluj-Napoca, Romania; University of Derby (Springer Science and Business Media LLC, 2021-05-05)
    We study the number of ordered k-partitions of a multiset with equal sums, having elements α1,…,αn and multiplicities m1,…,mn. Denoting this number by Sk(α1,…,αn;m1,…,mn), we find the generating function, derive an integral formula, and illustrate the results by numerical examples. The special case involving the set {1,…,n} presents particular interest and leads to the new integer sequences Sk(n), Qk(n), and Rk(n), for which we provide explicit formulae and combinatorial interpretations. Conjectures in connection to some superelliptic Diophantine equations and an asymptotic formula are also discussed. The results extend previous work concerning 2- and 3-partitions of multisets.
  • Mechanical Engineering Design, Does the Past Hold the key to the Future?

    Sole, Martin; Ian, Turner; Barber, Patrick; University of Derby (The Design Society, 2021)
    Industry design of a complex product has always required a cross-disciplinary team of experts. Is it possible to mimic these teams in academia when training the design engineers of the future, and what disciplinary skills will they possess? The exceptional collaboration potential provided by the internet means industry experts can work as a team, and at the same time, reside anywhere in the world. What are the capabilities of teamwork when the team members may never see each other for real? Though a physical prototype is sometimes required, most prototypes are designed and created in the virtual world using 3D modelling. The model can be tested, checked for accuracy, have materials applied, and be created parametrically which allows the products geometry to be reset to different sizes by the designer. Collaboration, effective communication and 3D modelling make it possible to design intricate and complex designs remotely. While we rightly congratulate ourselves on the complexity of modern design and how clever we have become, we must not lose sight of past achievements. Design has become more complex in this modern age, but it would be incorrect to say that complex design did not exist in times past. Before the internet, aircraft were built, global communication systems existed, men went to the moon. What can we learn, if anything, by looking at the methods used to design complex products in the past? How can we apply what we learnt from the past to the future?
  • Design Education - A Reversed Method to Fill and Information and Knowledge Gap Between Full-Time and Part-Time Students

    Sole, Martin; Barber, Patrick; Ian, Turner; University of Derby (The Design Society, 2021-08)
    Teachers in schools, tutors in colleges, and lecturers in universities are all required to have specific teaching qualifications. As part of the qualification, it is normal to study tried and tested pedological theories. Some examples are Bloom’s Taxonomy, Constructivism, and Experiential Learning. This paper identifies a gap in the information and knowledge required of student design engineers studying on a full-time course, when compared to part-time students. To redress this gap, it is suggested that no new theories are required but just a new method of applying an old theory, the application of Bloom’s Taxonomy in reverse alongside reverse engineering. An example of applying this method to a class of design engineers in their final year of a BEng (Hons) Mechanical Engineering is provided.
  • Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules

    Sepulevene, Luis; Drummond, Isabela; Kuehne, Bruno Tardiole; Frinhani, Rafael; Filho, Dionisio Leite; Peixoto, Maycon; Reiff-Marganiec, Stephan; Batista, Bruno; Federal University of Itajubá, Itajubá, Brazil; Federal University of Mato Grosso do Sul, Ponta Porã, Brazil; et al. (Oxford University Press, 2021-05-14)
    With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the fi nal result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is signi cant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using Machine Learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed, "With Knowledge" and "Without Knowledge", to explore the dataset using ML techniques to generate classi fiers that can assist in the fault diagnosis process. The approach "With Knowledge" performs the feature extraction manually, using the ML techniques: Random Forest, Naive Bayes, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP); on the other hand, the approach "Without Knowledge" performs an automatic feature extraction, through a Convolutional Neural Network (CNN). The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained respectively 88.43% and 54.98% for detection and detection and isolation of failures.
  • The effect of fine droplets on laminar propagation speed of a strained acetone-methane flame: Experiment and simulations

    Fan, Luming; Tian, Bo; Chong, Cheng Tung; Jaafar, Mohammad Nazri Mohd; Tanno, Kenji; McGrath, Dante; Oliveira, Pedro; Rogg, Bernd; Hochgreb, Simone; University of Derby; et al. (Elsevier, 2021-07-31)
    In this study, we investigate the effect of the presence of fuel droplets, their size and concentration, on stretched laminar flame speeds. We consider premixed strained methane/air mixtures, with the addition of small acetone droplets, and compare the flame velocity field behaviour to that of the fully vaporized mixture. An impinging stagnation flame configuration is used, to which a narrowly distributed polydisperse mist of acetone droplets is added. Total acetone molar concentrations between 9% and 20% per mole of methane are used, corresponding to 18.6% and 41.4% of the total fuel energy. The Sauter Mean Diameter (SMD) of acetone droplets is varied from 1.0 to 4.7 μm by carefully tuning the air flow rate passing through an atomizer. The droplet size distribution is characterized by a Phase Doppler Anamometry (PDA) system at the outlet of the burner. The flame propagation speed is measured using Particle Image Velocimetry (PIV) for overall equivalence ratios ranging from 0.8 to 1.4 at various strain rates, and the result is compared with a reference case in which acetone was fully vaporized. Unlike the fully vaporized flame, a two-stage reaction flame structure is observed for all droplet cases: a blue premixed flame front followed by a reddish luminous zone. Comparison of the results between gas-only and droplet-laden cases shows that the mean reference burning velocity of the mixture is significantly enhanced when droplets are present under rich cases, whereas the opposite is true for stoichiometric and lean cases. The mean droplet size also changes the relationship between flame speed and strain rate, especially for rich cases. The result suggests that with typical conditions found in laminar strained flames, even for the finest droplets that may have been vaporized before reaching the flame front, the resulting inhomogeneities may lead the flame to behaves differently from the well-premixed gaseous counterpart. Simulations at similar conditions are performed using a two-phase counterflow flame model to compare with experimental data. Model results of reference velocities do not compare well with observations, and the possible reasons for this behaviour are discussed, including the difficulties in determining the pre-vaporization process and thus the boundary conditions, as well as the fidelity of the current point-source based 1D model.
  • Thermal Fatigue Life of Ball Grid Array (BGA) Solder Joints Made From Different Alloy Compositions

    Depiver, Joshua Adeniyi; Sabuj, Mallik; Amalu, Emeka H; University of Derby; Teeside University (Elsevier, 2021-04-27)
    As temperature cycling drives fatigue failure of solder joints in electronic modules, characterisation of the thermal fatigue response of different solder alloy formulations in BGA solder joints functioning in mission-critical systems has become crucial. Four different lead-free and one eutectic lead-based solder alloys in BGA solder joints are characterised against their thermal fatigue lives (TFLs) to predict their mean-time-to-failure for preventive maintenance advice. Five finite elements (FE) models of the assemblies of the BGAs with the different solder alloy compositions are created with SolidWorks. The models are subjected to standard IEC 60749-25 temperature cycling in ANSYS mechanical package environment. Plastic strain, shear strain, plastic shear strain, and accumulated creep energy density responses of the solder joints are obtained and inputted into established life prediction models – Coffin Manson, Engelmaier, Solomon and Syed – to determine the lives of the models. SAC405 joints have the highest predicted TFL of circa 13.2 years, while SAC387 joints have the least life of circa 1.4 years. The predicted lives are inversely proportional to the magnitude of the areas of stress-strain hysteresis loops of the BGA solder joints. The prediction models are significantly not consistent in predicted magnitudes of TFLs across the solder joints. With circa 838% variation in the magnitudes of TFL predicted for Sn63Pb37, the damage parameters used in the models played a critical role and justifies that a combination of several failure modes drives solder joints damage. This research provides a technique for determining the preventive maintenance time of BGA components in mission-critical systems. It proposes developing a new life prediction model based on a combination of the damage parameters for improved prediction.
  • Detection of Cover Collapse Doline and Other Epikarst Features by Multiple Geophysical Techniques, Case Study of Tarimba Cave, Brazil

    Hussain, Yawar; Uagoda, Rogerio; Borges, Welitom; Prado, Renato; Hamza, Omar; Cárdenas-Soto, Martín; Havenith, Hans-Balder; Dou, Jie; Clemson University, Clemson, SC 29634, USA; University of Brasilia, Brasilia 70910-900, Brazil; et al. (MDPI, 2020-10-12)
    Reliable characterization of the karst system is essential for risk assessment where many associated hazards (e.g., cover-collapse dolines and groundwater pollution) can affect natural and built environments, threatening public safety. The use of multiple geophysical approaches may offer an improved way to investigate such cover-collapse sinkholes and aid in geohazard risk assessments. In this paper, covered karst, which has two types of shallow caves (vadose and fluvial) located in Tarimba (Goias, Brazil), was investigated using various geophysical methods to evaluate their efficiency in the delineation of the geometry of sediments filled sinkhole. The methods used for the investigation were Electrical Resistivity Tomography (ERT), Seismic Refraction Survey (SRS), Seismic Refraction Tomography (SRT) and the Very Low-Frequency Electromagnetic (VLF-EM) method. The study developed several (2D) sections of the measured physical properties, including P-wave velocity and electrical resistivity, as well as the induced current (because of local bodies). For the analysis and processing of the data obtained from these methods, the following approaches were adopted: ERT inversion using a least-square scheme, Karous-Hjelt filter for VLF-EM data and time-distance curves and Vp cross-sections for the SRS. The refraction data analysis showed three-layered stratigraphy topsoil, claystone and carbonate bedrock, respectively. The findings obtained from ERT (three-layered stratigraphy and sediment-filled doline), as well as VLF-EM (fractured or filled caves as a positive anomaly), were found to be consistent with the actual field conditions. However, the SRS and SRT methods did not show the collapsed material and reached the limited depth because of shorter profile lengths. The study provides a reasonable basis for the development of an integrated geophysical approach for site characterization of karst systems, particularly the perched tank and collapse doline.
  • On wind turbine power fluctuations induced by large-scale motions

    Ahmadi, Mohammad; Yang, Zhiyin; University of Derby (Elsevier, 2021-04-21)
    Our current understanding on the dynamic interaction between large-scale motions in the approaching turbulent flow and wind turbine power is very limited. To address this, numerical studies of a small-scale three-bladed horizontal axis wind turbine with cylinders placed in front of it to produce energetic coherent structures of varying scale relative to the turbine size have been carried out to examine the temporary variations of the turbine power. The predicted spectra reveal a strong interaction between large-scale turbulent motions generated by cylinders and the instantaneous turbine power. More specifically, it shows how the large dominant turbulent scales of incoming flow affect the spectral characteristics of turbine power, i.e, determining the level and trend of the turbine power spectrum. Comparisons reveal that there are two critical frequencies recognisable in the turbine power spectrum: the first one, close to the turbine rotational frequency, above which the coupling of upstream flow and turbine power disappears; the second one, identified for the first time and related to the dominant large-scale motions which dictate the level and trend of the turbine power spectrum. This study also shows that the strong scale-to-scale interaction between the upstream flow and turbine power reported previously does not appear at high Reynolds numbers.
  • Graph and Network Theory for the Analysis of Criminal Networks

    Cavallaro, Lucia; Bagdasar, Ovidiu; De Meo, Pasquale; Fumara, Giacomo; Liotta, Antonio; University of Derby; University of Messina, Italy; Free University of Bozen-Bolzano, Italy (Springer, Cham, 2021-02-19)
    Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridical acts, we have extracted data on the interactions among suspects within two Sicilian Mafia clans, obtaining two weighted undirected graphs. Then, we have investigated the roles of these weights on the criminal networks properties, focusing on two key features: weight distribution and shortest path length. We also present an experiment that aims to construct an artificial network which mirrors criminal behaviours. To this end, we have conducted a comparative degree distribution analysis between the real criminal networks, using some of the most popular artificial network models: Watts-Strogats, Erdős-Rényi, and Barabási-Albert, with some topology variations. This chapter will be a valuable tool for researchers who wish to employ social network analysis within their own area of interest.
  • An LMI Approach-Based Mathematical Model to Control Aedes aegypti Mosquitoes Population via Biological Control

    Dianavinnarasi, J.; Raja, R.; Alzabut, J.; Niezabitowski, M.; Selvam, G.; Bagdasar, O.; Alagappa University, Karaikudi 630 004, India; Prince Sultan University, Riyadh 12435, Saudi Arabia; Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland; Vinayaka Missions University, Salem 636308, India; et al. (Hindawi Limited, 2021-03-09)
    In this paper, a novel age-structured delayed mathematical model to control Aedes aegypti mosquitoes via Wolbachia-infected mosquitoes is introduced. To eliminate the deadly mosquito-borne diseases such as dengue, chikungunya, yellow fever, and Zika virus, the Wolbachia infection is introduced into the wild mosquito population at every stage. This method is one of the promising biological control strategies. To predict the optimal amount of Wolbachia release, the time varying delay is considered. Firstly, the positiveness of the solution and existence of both Wolbachia present and Wolbachia free equilibrium were discussed. Through linearization, construction of suitable Lyapunov–Krasovskii functional, and linear matrix inequality theory (LMI), the exponential stability is also analyzed. Finally, the simulation results are presented for the real-world data collected from the existing literature to show the effectiveness of the proposed model.
  • Biodiesel sustainability: The global impact of potential biodiesel production on the energy–water–food (EWF) nexus

    Chong, Cheng Tung; Loe, Ting Yu; Wong, Kang Yao; Ashokkumar, Veeramuthu; Lam, Su Shiung; Chong, Wen Tong; Borrion, Aiduan; Tian, Bo; Ng, Jo-Han; Shanghai Jiao Tong University, Lingang, Shanghai 201306, China; et al. (Elsevier, 2021-02-01)
    A data-driven model is used to analyse the global effects of biodiesel on the energy–water–food (EWF) nexus, and to understand the complex environmental correlation. Several criteria to measure the sustainability of biodiesel and four main limiting factors for biodiesel production are discussed in this paper. The limiting factors includes water stress, food stress, feedstock quantity and crude oil price. The 155-country model covers crude oil prices ranging from USD10/bbl to USD160/bbl, biodiesel refinery costs ranging from -USD0.30/L to USD0.30/L and 45 multi-generation biodiesel feedstocks. The model is capable of ascertaining changes arising from biodiesel adoption in terms of light-duty diesel engine emissions (NO, CO, UHC and smoke opacity), water stress index (WSI), dietary energy supply (DES), Herfindahl–Hirschman index (HHI) and short-term energy security. With the addition of potential biodiesel production, the renewable energy sector of global primary energy profile can increase by 0.43%, with maximum increment up to 10.97% for Malaysia. At current crude oil price of USD75/bbl and refinery cost of USD0.1/L, only Benin, Ireland and Togo can produce biodiesel profitably. The model also shows that water requirement varies non-linearly with multi-feedstock biodiesel production as blending ratio increases. Out of the 155 countries, biodiesel production is limited by feedstock quantity for 82 countries, 47 are limited by crude oil price, 20 by water stress and 6 by food stress. The results provide insights for governments to set up environmental policy guidelines, in implementing biodiesel technology as a cleaner alternative to diesel.
  • Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

    Almomani, Ammar; Nawasrah, Ahmad Al; Alauthman, Mohammad; Betar, Mohammed Azmi Al; Meziane, Farid; Al-Balqa Applied University, Irbid, Jordan; Taibah University, Median, Saudia Arabia; Zarqa University, Jordan; University of Derby (Inderscience, 2021-01-28)
    A botnet refers to a group of machines. These machines are controlled distantly by a specific attacker. It represents a threat facing the web and data security. Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities. It has been engaged by bot herders for increasing the lifetime of malicious servers through changing the IP addresses of the domain name quickly. In the present research, we aimed to propose a new system. This system is named fast flux botnet catcher system (FFBCS). This system can detect FF-domains in an online mode using an adaptive dynamic evolving spiking neural network algorithm. Comparing with two other related approaches the proposed system shows a high level of detection accuracy, low false positive and negative rates, respectively. It shows a high performance. The algorithm's proposed adaptation increased the accuracy of the detection. For instance, this accuracy reached (98.76%) approximately.
  • Self-healing of bio-cementitious mortar incubated within neutral and acidic soil

    Esaker, Mohamed; Hamza, Omar; Souid, Adam; Elliott, David; University of Derby (Springer Science and Business Media LLC, 2021-04-14)
    The efficiency of bio self-healing of pre-cracked mortar specimens incubated in sand was investigated. The investigation examined the effect of soil pH representing industrially recognised classes of exposure, ranging from no risk of chemical attack (neutral pH≈7) to very high risk (pH≈4.5). Simultaneously, the soil was subjected to fully and partially saturated cycles for 120 days to resemble groundwater-level fluctuation. Bacillus Subtilis with nutrients were impregnated into perlite and utilised as a bacterial healing agent. The healing agent was added to half of the mortar specimens for comparison purposes. Mineral precipitations were observed in both control and bio-mortar specimens, and the healing products were examined by SEM-EDX scanning. The healing ratio was evaluated by comparing (i) the repair rate of the crack area and (ii) by capillary water absorption and sorptivity index - before and after incubation. The results indicated that bacteria-doped specimens (bio-mortar) exhibited the most efficient crack-healing in all incubation conditions i.e. different chemical exposure classes. In the pH neutral soil, the average healing ratios for the control and bio-mortar specimens were 38% and 82%, respectively. However, the healing ratio decreased by 43% for specimens incubated in acidic soil (pH≈4) compared with specimens incubated in neutral soil (pH≈7). The study implies that bio self-healing is generally beneficial for concrete embedded within the soil; however, aggressive ground conditions can inhibit the healing process.
  • Severity Estimation of Plant Leaf Diseases Using Segmentation Method

    Entuni, Chyntia Jaby; Afendi Zulcaffle, Tengku Mohd; Kipli, Kuryati; Kurugollu, Fatih; Universiti Malaysia Sarawak, Malaysia; University of Derby (2020-11-09)
    Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that is 34.75s in average with 0.2697s standard deviation.
  • Controlling Wolbachia transmission and invasion dynamics among aedes aegypti population via impulsive control strategy

    Dianavinnarasi, Joseph; Raja, Ramachandran; Alzabut, Jehad; Niezabitowski, Michał; Bagdasar, Ovidiu; Alagappa University, Karaikudi, India; Prince Sultan University, Riyadh, Saudi Arabia; Silesian University of Technology, Akademicka 16, Gliwice, Poland; University of Derby (MDPI AG, 2021-03-08)
    This work is devoted to analyzing an impulsive control synthesis to maintain the self-sustainability of Wolbachia among Aedes Aegypti mosquitoes. The present paper provides a fractional order Wolbachia invasive model. Through fixed point theory, this work derives the existence and uniqueness results for the proposed model. Also, we performed a global Mittag-Leffler stability analysis via Linear Matrix Inequality theory and Lyapunov theory. As a result of this controller synthesis, the sustainability of Wolbachia is preserved and non-Wolbachia mosquitoes are eradicated. Finally, a numerical simulation is established for the published data to analyze the nature of the proposed Wolbachia invasive model.
  • Targeted ensemble machine classification approach for supporting IOT enabled skin disease detection

    Yu, Hong Qing; Reiff-Marganiec, Stephan; University of Derby (IEEE, 2021-03-26)
    The fast development of the Internet of Things (IoT) changes our life in many areas, especially in the health domain. For example, remote disease diagnosis can be achieved more efficiently with advanced IoT-technologies which not only include hardware but also smart IoT data processing and learning algorithms, e.g. image-based disease classification. In this paper, we work in a specific area of skin condition classification. This research work aims to provide an implementable solution for IoT-led remote skin disease diagnosis applications. The research output can be concluded into three folders. The first folder is about dynamic AI model configuration supported IoT-Fog-Cloud remote diagnosis architecture with hardware examples. The second folder is the evaluation survey regarding the performances of machine learning models for skin disease detection. The evaluation contains a variety of data processing methods and their aggregations. The evaluation takes account of both training-testing and cross-testing validations on all seven conditions and individual condition. In addition, the HAM10000 dataset is picked for the evaluation process according to the suitability comparisons to other relevant datasets. In the evaluation, we discuss the earlier work of ANN, SVM and KNN models, but the evaluation process mainly focuses on six widely applied Deep Learning models of VGG16, Inception, Xception, MobileNet, ResNet50 and DenseNet161. The result shows that each of the top four models for the major seven skin conditions has better performance for the specific condition than others. Based on the evaluation discovery, the last folder proposes a novel classification approach of the Targeted Ensemble Machine Classify Model (TEMCM) to enable dynamically combining a suitable model in a two-phase detection process. The final evaluation result shows the proposed model can archive better performance.
  • Application of caputo–fabrizio operator to suppress the aedes aegypti mosquitoes via wolbachia: an LMI approach

    Dianavinnarasi, J.; Raja, R.; Alzabut, J.; Cao, J.; Niezabitowski, M.; Bagdasar, O.; Alagappa University, Karaikudi, India; Prince Sultan University, Riyadh 12435, Saudi Arabia; Southeast University, Nanjing, China; Yonsei University, Seoul, South Korea; et al. (Elsevier BV, 2021-02-11)
    The aim of this paper is to establish the stability results based on the approach of Linear Matrix Inequality (LMI) for the addressed mathematical model using Caputo–Fabrizio operator (CF operator). Firstly, we extend some existing results of Caputo fractional derivative in the literature to a new fractional order operator without using singular kernel which was introduced by Caputo and Fabrizio. Secondly, we have created a mathematical model to increase Cytoplasmic Incompatibility (CI) in Aedes Aegypti mosquitoes by releasing Wolbachia infected mosquitoes. By this, we can suppress the population density of A.Aegypti mosquitoes and can control most common mosquito-borne diseases such as Dengue, Zika fever, Chikungunya, Yellow fever and so on. Our main aim in this paper is to examine the behaviours of Caputo–Fabrizio operator over the logistic growth equation of a population system then, prove the existence and uniqueness of the solution for the considered mathematical model using CF operator. Also, we check the alpha-exponential stability results for the system via linear matrix inequality technique. Finally a numerical example is provided to check the behaviour of the CF operator on the population system by incorporating the real world data available in the known literature.

View more