Search results for: quantum genetic algorithm

Authors: Md. Shafiullah, Ali T. Al-Awami

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This project proposes optimal scheduling of imported power of a load aggregator with the utilization of EVs to maximize its profit. As with the increase of renewable energy resources, electricity price in competitive market becomes more uncertain and, on the other hand, with the penetration of renewable distributed generators in the distribution network the predicted load of a load aggregator also becomes uncertain in real time. Though there is uncertainties in both load and price, the use of EVs storage capacity can make the operation of load aggregator flexible. LA submits its offer to day-ahead market based on predicted loads and optimized use of its EVs to maximize its profit, as well as in real time operation it uses its energy storage capacity in such a way that it can maximize its profit. In this project, load aggregators profit maximization algorithm is formulated and the optimization problem is solved with the help of CVX. As in real time operation the forecasted loads differ from actual load, the mismatches are settled in real time balancing market. Simulation results compare the profit of a load aggregator with a hypothetical group of 1000 EVs and without EVs.

Keywords: CVX, electricity market, load aggregator, load and price uncertainties, profit maximization, real time balancing operation

Procedia PDF Downloads 394

Authors: S. Sreeja, Radhakrishnan R. Nair, Noble Gracious, Sreeja S. Nair, M. Radhakrishna Pillai

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Background: Tacrolimus is a potent immunosuppressant clinically used for the long term treatment of antirejection of transplanted organs in liver and kidney transplant recipients though dose optimization is poorly managed. However, So far no study has been carried out on the South Indian kidney transplant patients. The objective of this study is to evaluate the potential influence of a functional polymorphism in CYP3A5*3 gene on tacrolimus physiological availability/dose ratio in South Indian renal transplant patients. Materials and Methods: Twenty five renal transplant recipients receiving tacrolimus were enrolled in this study. Their body weight, drug dosage, and therapeutic concentration of Tacrolimus were observed. All patients were on standard immunosuppressive regime of Tacrolimus-Mycophenolate mofetil along with steroids on a starting dose of Tac 0.1 mg/kg/day. CYP3A5 genotyping was performed by PCR followed with RFLP. Conformation of RFLP analysis and variation in the nucleotide sequence of CYP3A5*3 gene were determined by direct sequencing using a validated automated generic analyzer. Results: A significant association was found between tacrolimus per dose/kg/d and CYP3A5 gene (A6986G) polymorphism in the study population. The CYP3A5 *1/*1, *1/*3 and *3/*3 genotypes were detected in 5 (20 %), 5 (20 %) and 15 (60 %) of the 25 graft recipients, respectively. CYP3A5*3 genotypes were found to be a good predictor of tacrolimus Concentration/Dose ratio in kidney transplant recipients. Significantly higher L/D was observed among non-expressors 9.483 ng/mL(4.5- 14.1) as compared with the expressors 5.154 ng/mL (4.42-6.5 ) of CYP3A5. Acute rejection episodes were significantly higher for CYP3A5*1 homozygotes compared to patients with CYP3A5*1/*3 and CYP3A5*3/*3 genotypes (40 % versus 20 % and 13 %, respectively ). The dose normalized TAC concentration (ng/ml/mg/kg) was significantly lower in patients having CYP3A5*1/*3 polymorphism. Conclusion: This is the first study to extensively determine the effect of CYP3A5*3 genetic polymorphism on tacrolimus pharmacokinetics in South Indian renal transplant recipients and also shows that majority of our patients carry mutant allele A6986G in CYP3A5*3 gene. Identification of CYP3A5 polymorphism prior to transplantation could contribute to evaluate the appropriate initial dosage of tacrolimus for each patient.

Keywords: kidney transplant patients, CYP3A5 genotype, tacrolimus, RFLP

Procedia PDF Downloads 278

Authors: Deepshikha Shukla, C. H. Solanki, Mayank K. Desai

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Amongst all the natural hazards, earthquakes have the potential for causing the greatest damages. Since the earthquake forces are random in nature and unpredictable, the quantification of the hazards becomes important in order to assess the hazards. The time and place of a future earthquake are both uncertain. Since earthquakes can neither be prevented nor be predicted, engineers have to design and construct in such a way, that the damage to life and property are minimized. Seismic hazard analysis plays an important role in earthquake design structures by providing a rational value of input parameter. In this paper, both mathematical, as well as computational methods adopted by researchers globally in the past five years, will be discussed. Some mathematical approaches involving the concepts of Poisson’s ratio, Convex Set Theory, Empirical Green’s Function, Bayesian probability estimation applied for seismic hazard and FOSM (first-order second-moment) algorithm methods will be discussed. Computational approaches and numerical model SSIFiBo developed in MATLAB to study dynamic soil-structure interaction problem is discussed in this paper. The GIS-based tool will also be discussed which is predominantly used in the assessment of seismic hazards.

Keywords: computational methods, MATLAB, seismic hazard, seismic measurements

Procedia PDF Downloads 313

Authors: Yue Yang, Qiang Wu, Xingyu Gao

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"Organized research units" have always been an important part of academia. According to the type of organization, there are public research units, university research units, and corporate research units. Existing research has explored the research unit in some depth from several perspectives. However, there is a research gap on the closer interaction between the three from a network perspective and the impact of this interaction on their performance as well as originality. Cliques are a special kind of structure under the concept of cohesive subgroups in the field of social networks, representing particularly tightly knit teams in a network. This study develops the concepts of the diversity of clique types and the diversity of clique geography based on cliques, starting from the diversity of collaborative activities characterized by them. Taking research units as subjects and assigning values to their power in cliques based on occupational age, we explore the impact of clique diversity and clique power on their performance as well as originality and the moderating role of clique relationship strength and structural holes in them. By collecting 9094 articles published in the field of quantum communication at WoSCC over the 15 years 2007-2021, we processed them to construct annual collaborative networks between a total of 533 research units and measured the network characteristic variables using Ucinet. It was found that the type and geographic diversity of cliques promoted the performance and originality of the research units, and the strength of clique relationships positively moderated the positive effect of the diversity of clique types on performance and negatively affected the promotional relationship between the geographic diversity of cliques and performance. It also negatively affected the positive effects of clique-type diversity and clique-geography diversity on originality. Structural holes positively moderated the facilitating effect of both types of factional diversity on performance and originality. Clique power promoted the performance of the research unit, but unfavorably affected its performance on novelty. Faction relationship strength facilitated the relationship between faction rights and performance and showed negative insignificance for clique power and originality. Structural holes positively moderated the effect of clique power on performance and originality.

Keywords: research unit, social networks, clique structure, clique power, diversity

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Authors: K. M. Damodara Gowda

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Background: Maternal health and nutrition are considered as the predominant factors influencing brain functional development. If the mother is free of illness and genetic defects, maternal nutrition would be one of the most critical factors affecting the brain development. Calorie restrictions cause significant impairment in spatial learning ability and the levels of Brain Derived Neurotrophic Factor (BDNF) in rats. But, the mechanism by which the prenatal under-nutrition leads to impairment in brain learning and memory function is still unclear. In the present study, prenatal Astaxanthin supplementation on BDNF level, spatial learning and memory performance in the offspring’s of normal, calorie restricted and Astaxanthin supplemented rats was investigated. Methodology: The rats were administered with 6mg and 12 mg of astaxanthin /kg bw for 21 days following which acquisition and retention of spatial memory was tested in a partially-baited eight arm radial maze. The BDNF level in different regions of the brain (cerebral cortex, hippocampus and cerebellum) was estimated by ELISA method. Results: Calorie restricted animals treated with astaxanthin made significantly more correct choices (P < 0.05), and fewer reference memory errors (P < 0.05) on the tenth day of training compared to offsprings of calorie restricted animals. Calorie restricted animals treated with astaxanthin also made significantly higher correct choices (P < 0.001) than untreated calorie restricted animals in a retention test 10 days after the training period. The mean BDNF level in cerebral cortex, Hippocampus and cerebellum in Calorie restricted animals treated with astaxanthin didnot show significant variation from that of control animals. Conclusion: Findings of the study indicated that memory and learning was impaired in the offspring’s of calorie restricted rats which was effectively modulated by astaxanthin at the dosage of 12 mg/kg body weight. In the same way the BDNF level at cerebral cortex, Hippocampus and Cerebellum was also declined in the offspring’s of calorie restricted animals, which was also found to be effectively normalized by astaxanthin.

Keywords: calorie restiction, learning, Memory, Cerebral cortex, Hippocampus, Cerebellum, BDNF, Astaxanthin

Procedia PDF Downloads 212

Authors: Nandita Ghosh, Shinjini Mitra, Ena Ray Banerjee

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Atopic dermatitis (AD) is a chronic disease of the skin, involving itchy, reddish, and scaly lesions. It mainly affects children and has a high prevalence in developing countries. The AD may occur due to environmental or genetic factors. There is no permanent cure for the AD. Currently, all therapeutic strategies involve methods to simply alleviate the symptoms, and include lotions and corticosteroids, which have adverse effects. Use of phytochemicals and natural products has not yet been exploited fully. The particle used in this study is derived from Cyamopsis tetragonoloba, an edible polysaccharide with a galactomannan component. The mannose component mainly increases its specificity towards cellular uptake by mannose receptors, highly expressed by the macrophage. The aim of this study was to determine the therapeutic effect of guar gum nanoparticles (GN) in vitro and in vivo in the AD. To assess the wound healing capacity of the guar gum nanoparticle (GN), we first treated adherent NIH3T3 cells, with a scratch injury, with GN. GN successfully healed the wound caused by the scratch. In the in vivo experiment, Balb/c mice ear were topically treated with oxazolone (oxa) to induce AD and then were topically treated with GN. The ear thickness was increased significantly till day 28 on the treatment of Oxa. The GN application showed a significant decrease in the thickness as assessed on day 28. The total cell count of skin cells showed fold increase when treated with oxa, was again decreased on topical application of GN on the affected skin. The eosinophil count, as assessed by Giemsa staining was also increased when treated with oxa, GN application led to a significant decrease. The IgE level was assessed in the serum samples which showed that GN helped in restoring the alleviated IgE level. The T helper cells and the macrophage population showed increased percentage when treated with oxa, the GN application. This was examined by flow cytometry. The H&E staining of the ear tissue showed epidermal thickness in the oxa treated mice, GN application showed reduced cellular filtration followed by epidermal thickness. Thus our assays showed that GN was successful in alleviating the disease caused by Oxa when administered topically.

Keywords: allergen, inflammation, nanodrug, wound

Procedia PDF Downloads 221

Authors: Nefzi Ahlem, Aydi Ben Abdallah Rania, Jabnoun-Khiareddine Hayfa, Ammar Nawaim, Mejda Daami-Remadi

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Fusarium Crown and Root Rot (FCRR) caused by Fusarium oxysporum f. sp. radicis-lycopersici (FORL) is a serious tomato (Solanum lycopersicum L.) disease in Tunisia. Its management is very difficult due to the long survival of its resting structures and to the luck of genetic resistance. In this work, we explored the wild Solanaceae species Withania somnifera, growing in the Tunisian Centre-East, as a potential source of biocontrol agents effective in FCRR suppression and tomato growth promotion. Seven fungal isolates were shown able to colonize tomato roots, crowns, and stems. Used as conidial suspensions or cell-free culture filtrates, all tested fungal treatments significantly enhanced tomato growth parameters by 21.5-90.3% over FORL-free control and by 27.6-93.5% over pathogen-inoculated control. All treatments significantly decreased the leaf and root damage index by 28.5-92.8 and the vascular browning extent 9.7-86.4% over FORL-inoculated and untreated control. The highest disease suppression ability (decrease by 86.4-92.8% in FCRR severity) over pathogen-inoculated control and by 81.3-88.8 over hymexazol-treated control) was expressed by I6 based treatments. This endophytic fungus was morphologically characterized and identified using rDNA sequencing gene as Fusarium sp. I6 (MG835371). This fungus was shown able to reduce FORL radial growth by 58.5–83.2% using its conidial suspension or cell-free culture filtrate. Fusarium sp. I6 showed chitinolytic, proteolytic and amylase activities. The current study clearly demonstrated that Fusarium sp. (I6) is a promising biocontrol candidate for suppressing FCRR severity and promoting tomato growth. Further investigations are required for elucidating its mechanism of action involved in disease suppression and plant growth promotion.

Keywords: antifungal activity, associated fungi, Fusarium oxysporum f. sp. radicis-lycopersici, Withania somnifera, tomato growth

Procedia PDF Downloads 125

Authors: Fang Liu, JunShuai Xue, JiaJia Yao, GuanLin Wu, ZuMaoLi, XueYan Yang, HePeng Zhang, ZhiPeng Sun

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Terahertz spectra is in great demand since last two decades for many photonic and electronic applications. III-Nitride resonant tunneling diode is one of the promising candidates for portable and compact THz sources. Room temperature microwave oscillator based on GaN/AlN resonant tunneling diode was reported in this work. The devices, grown by plasma-assisted molecular-beam epitaxy on free-standing c-plane GaN substrates, exhibit highly repeatable and robust negative differential resistance (NDR) characteristics at room temperature. To improve the interface quality at the active region in RTD, indium surfactant assisted growth is adopted to enhance the surface mobility of metal atoms on growing film front. Thanks to the lowered valley current associated with the suppression of threading dislocation scattering on low dislocation GaN substrate, a positive peak current density of record-high 2.1 MA/cm2 in conjunction with a peak-to-valley current ratio (PVCR) of 1.2 are obtained, which is the best results reported in nitride-based RTDs up to now considering the peak current density and PVCR values simultaneously. When biased within the NDR region, microwave oscillations are measured with a fundamental frequency of 0.31 GHz, yielding an output power of 5.37 µW. Impedance mismatch results in the limited output power and oscillation frequency described above. The actual measured intrinsic capacitance is only 30fF. Using a small-signal equivalent circuit model, the maximum intrinsic frequency of oscillation for these diodes is estimated to be ~260GHz. This work demonstrates a microwave oscillator based on resonant tunneling effect, which can meet the demands of terahertz spectral devices, more importantly providing guidance for the fabrication of the complex nitride terahertz and quantum effect devices.

Keywords: GaN resonant tunneling diode, peak current density, microwave oscillation, intrinsic capacitance

Procedia PDF Downloads 107

Authors: Mohit Kumar, Mayur Betharia

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Healthcare domain, with the emergence of Electronic Medical Record (EMR), collects a lot of data which have been attracting Data Mining expert’s interest. In the past, doctors have relied on their intuition while making critical clinical decisions. This paper presents the means to analyze the Medical workflows to get business insights out of huge dumped medical databases. Market Basket Analysis (MBA) which is a special data mining technique, has been widely used in marketing and e-commerce field to discover the association between products bought together by customers. It helps businesses in increasing their sales by analyzing the purchasing behavior of customers and pitching the right customer with the right product. This paper is an attempt to demonstrate Market Basket Analysis applications in healthcare. In particular, it discusses the Market Basket Analysis Algorithm ‘Apriori’ applications within healthcare in major areas such as analyzing the workflow of diagnostic procedures, Up-selling and Cross-selling of Healthcare Systems, designing healthcare systems more user-friendly. In the paper, we have demonstrated the MBA applications using Angiography Systems, but can be extrapolated to other modalities as well.

Keywords: data mining, market basket analysis, healthcare applications, knowledge discovery in healthcare databases, customer relationship management, healthcare systems

Procedia PDF Downloads 141

Authors: Sorin Muraru

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Computational generation of carbon nanostructures is still a very demanding process. This work provides an alternative to manual molecular modeling through an algorithm meant to automate the design of such structures. Specifically, carbon nanochimneys are obtained through the bonding of a carbon nanotube with the smaller edge of an open carbon nanocone. The methods of connection rely on mathematical, geometrical and chemical properties. Non-hexagonal rings are used in order to perform the correct bonding of dangling bonds. Once obtained, they are useful for thermal transport, gas storage or other applications such as gas separation. The carbon nanochimneys are meant to produce a less steep connection between structures such as the carbon nanotube and graphene sheet, as in the pillared graphene, but can also provide functionality on its own. The method relies on connecting dangling bonds at the edges of the two carbon nanostructures, employing the use of two different types of auxiliary structures on a case-by-case basis. The code is implemented in Python 3.7 and generates an output file in the .pdb format containing all the system’s coordinates. Acknowledgment: This work was supported by a grant of the Executive Agency for Higher Education, Research, Development and innovation funding (UEFISCDI), project number PN-III-P1-1.1-TE-2016-24-2, contract TE 122/2018.

Keywords: carbon nanochimneys, computational, carbon nanotube, carbon nanocone, molecular modeling, carbon nanostructures

Procedia PDF Downloads 142

Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

Procedia PDF Downloads 104

Authors: Ehsan Mehryaar, Reza Bushehri

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One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

Procedia PDF Downloads 170

Authors: Chukwuma Mgboji

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Since the advent of Artificial Intelligence, robots have become a major stay in developing societies. Robots are deployed in Education, Health, Food and in other spheres of life. Nigeria a country in West Africa has a very low profile in the advancement of Artificial Intelligence especially in the grass roots. The benefits of Artificial intelligence are not fully maximised and harnessed. Advances in artificial intelligence are perceived as impossible or observed as irrelevant. This study seeks to ascertain the needed skills for the development of artificialintelligence amongst secondary schools in Nigeria. The study focused on South East Nigeria with Five states namely Imo, Abia, Ebonyi, Anambra and Enugu. The sample size is 1000 students drawn from Five Government owned Universities offering Computer Science, Computer Education, Electronics Engineering across the Five South East states. Survey method was used to solicit responses from respondents. The findings from the study identified mathematical skills, analytical skills, problem solving skills, computing skills, programming skills, algorithm skills amongst others. The result of this study to the best of the author’s knowledge will be highly beneficial to all stakeholders involved in the advancements and development of artificial intelligence.

Keywords: artificial intelligence, secondary school, robotics, skills

Procedia PDF Downloads 121

Authors: Jeremiah L. Chukwuneke, Obiora E. Anisiji, Chinonso H. Achebe, Paul C. Okolie

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This paper developed a mathematical model of a commercial biogas plant for urban area clean energy requirement. It identified biodegradable waste materials like domestic/city refuse as economically viable alternative source of energy. The mathematical formulation of the proposed gas plant follows the fundamental principles of thermodynamics, and further analyses were accomplished to develop an algorithm for evaluating the plant performance preferably in terms of daily production capacity. In addition, the capacity of the plant is equally estimated for a given cycle of operation and presented in time histories. A nominal 1500 m3 power gas plant was studied characteristically and its performance efficiency evaluated. It was observed that the rate of bio gas production is essentially a function of the reactor temperature, pH, substrate concentration, rate of degradation of the biomass, and the accumulation of matter in the system due to bacteria growth. The results of this study conform to a very large extent with reported empirical data of some existing plant and further model validations were conducted in line with classical records found in literature.

Keywords: energy and mass conservation, specific growth rate, thermophilic bacteria, temperature, rate of bio gas production

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Authors: Yuanxiang Miao

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Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

Procedia PDF Downloads 102

Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

Procedia PDF Downloads 72

Authors: E. Pretorius, A. M. Viljoen, M. van der Bank

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Introduction: The phytochemistry of Agathosma crenulata and A. betulina has been studied extensively, while their molecular analysis through DNA barcoding remains virtually unexplored. This technique can confirm the identity of plant species included in a herbal product, thereby ensuring the efficacy of the herbal product and the accuracy of its label. Materials and methods: Authentic Agathosma reference material of A. betulina (n=16) and A. crenulata (n=10) were obtained. Thirteen commercial products were purchased from various health shops around Johannesburg, South Africa, using the search term “Agathosma” or “Buchu.” The plastid regions matK and ycf1 were used to barcode the Buchu products, and BRONX analysis confirmed the taxonomic identity of the samples. UPLC-MS analyses were also performed. Results: Only (30/60) 60% of the traded samples tested from 13 suppliers contained A. betulina in their herbal products. Similar results were also obtained for the UPLC-MS analysis. Conclusion: In this study, we demonstrate the application of DNA barcoding in combination with phytochemical analysis to authenticate herbal products claiming to contain Agathosma plants as an ingredient in their products. This supports manufacturing efforts to ensure that herbal products that are safe for the consumer.

Keywords: Buchu, substitution, barcoding, BRONX algorithm, matK, ycf1, UPLC-MS

Procedia PDF Downloads 107

Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

Procedia PDF Downloads 168

Authors: Sunil Kumar, Uday C. Ghoshal

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Background and aims: Serotonin (5-hydroxtryptamine, 5-HT) is an important factor in gut function, playing key roles in intestinal peristalsis and secretion, and in sensory signaling in the brain-gut axis. Removal from its sites of action is mediated by a specific protein called the serotonin reuptake transporter (SERT). Polymorphisms in the promoter region of the SERT gene have effects on transcriptional activity, resulting in altered 5-HT reuptake efficiency. Functional polymorphisms may underlie disturbance in gut function in individuals suffering with disorders such as irritable bowel syndrome (IBS). The aim of this study was to assess the potential association between SERT polymorphisms and the diarrhea predominant IBS (D-IBS) phenotype Subjects: A total of 36 northern Indian female patients and 55 female northern Indian healthy controls (HC) were subjected to genotyping. Methods: Leucocyte DNA of all subjects was analyzed by polymerase chain reaction based technologies for SERT polymorphisms, specifically the insertion/deletion polymorphism in the promoter (SERT-P). Statistical analysis was performed to assess association of SERT polymorphism allele with the D-IBS phenotype. Results: The frequency of distribution of SERT-P gene was comparable between female patients with IBS and HC (p = 0.086). However, frequency of SERT-P deletion/deletion genotype was significantly higher in female patients with D-IBS compared to C-IBS and A-IBS [17/19 (89.5%) vs. 4/12 (33.3%) vs. 1/5 (20%), p=0.001, respectively]. The mucosal level of serotonin was higher in D-IBS compared to C-IBS and A-IBS [Median, range (159.26, 98.78–212.1) vs. 110.4, 67.87–143.53 vs. 92.34, 78.8–166.3 pmol/mL, p=0.001, respectively]. The mucosal level of serotonin was higher in female patients with IBS with SERT-P deletion/deletion genotype compared deletion/insertion and insertion/insertion [157.65, 67.87–212.1 vs. 110.4, 78.1–143.32 vs. 100.5, 69.1–132.03 pmol/mL, p=0.001, respectively]. Patients with D-IBS with deletion/deletion genotype more often reported symptoms of abdominal pain, discomfort (p=0.025) and bloating (p=0.039). Symptoms development following lactose ingestion was strongly associated with D-IBS and SERT-P deletion/deletion genotype (p=0.004). Conclusions: Significant association was observed between D-IBS and the SERT-P deletion/deletion genotype, suggesting that the serotonin transporter is a potential candidate gene for D-IBS in women.

Keywords: serotonin, SERT, inflammatory bowel disease, genetic polymorphism

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Authors: Ecem Deniz Kırkpantur, Ozge Ecmel Onur, Tuba Cimilli Ozturk, Ebru Unal Akoglu

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Moyamoya disease is a unique chronic progressive cerebrovascular disease characterized by bilateral stenosis or occlusion of the arteries around the circle of Willis with prominent arterial collateral circulation. The occurrence of Moyamoya disease is related to immune, genetic and other factors. There is no curative treatment for Moyamoya disease. Secondary prevention for patients with symptomatic Moyamoya disease is largely centered on surgical revascularization techniques. We present here a 62-year old male presented with headache and vision loss for 2 days. He was previously diagnosed with hypertension and glaucoma. On physical examination, left eye movements were restricted medially, both eyes were hyperemic and their movements were painful. Other neurological and physical examination were normal. His vital signs and laboratory results were within normal limits. Computed tomography (CT) showed dilated vascular structures around both lateral ventricles and atherosclerotic changes inside the walls of internal carotid artery (ICA). Magnetic resonance imaging (MRI) and angiography (MRA) revealed dilated venous vascular structures around lateral ventricles and hyper-intense gliosis in periventricular white matter. Ischemic gliosis around the lateral ventricles were present in the Digital Subtracted Angiography (DSA). After the neurology, ophthalmology and neurosurgery consultation, the patient was diagnosed with Moyamoya disease, pulse steroid therapy was started for vision loss, and super-selective DSA was planned for further investigation. Moyamoya disease is a rare condition, but it can be an important cause of stroke in both children and adults. It generally affects anterior circulation, but posterior cerebral circulation may also be affected, as well. In the differential diagnosis of acute vision loss, occipital stroke related to Moyamoya disease should be considered. Direct and indirect surgical revascularization surgeries may be used to effectively revascularize affected brain areas, and have been shown to reduce risk of stroke.

Keywords: headache, Moyamoya disease, stroke, visual loss

Procedia PDF Downloads 247

Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Diya Kohli, Amalia Ardeljan, Lexi Frankel, Jose Garcia, Lokesh Manjani, Omar Rashid

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Gonorrhea is the second most common sexually transmitted disease (STDs) in the United States of America. Gonorrhea affects the urethra, rectum, or throat and the cervix in females. Lymphoma is a cancer of the immune network called the lymphatic system that includes the lymph nodes/glands, spleen, thymus gland, and bone marrow. Lymphoma can affect many organs in the body. When a lymphocyte develops a genetic mutation, it signals other cells into rapid proliferation that causes many mutated lymphocytes. Multiple studies have explored the incidence of cancer in people infected with STDs such as Gonorrhea. For instance, the studies conducted by Wang Y-C and Co., as well as Caini, S and Co. established a direct co-relationship between Gonorrhea infection and incidence of prostate cancer. We hypothesize that Gonorrhea infection also increases the incidence of Lymphoma in patients. This research study aimed to evaluate the correlation between Gonorrhea infection and the incidence of Lymphoma. The data for the research was provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database. This database was utilized to evaluate patients infected with Gonorrhea versus the ones who were not infected to establish a correlation with the prevalence of Lymphoma using ICD-10 and ICD-9 codes. Access to the database was granted by the Holy Cross Health, Fort Lauderdale for academic research. Standard statistical methods were applied throughout. Between January 2010 and December 2019, the query was analyzed and resulted in 254 and 808 patients in both the infected and control group, respectively. The two groups were matched by Age Range and CCI score. The incidence of Lymphoma was 0.998% (254 patients out of 25455) in the Gonorrhea group (patients infected with Gonorrhea that was Lymphoma Positive) compared to 3.174% and 808 patients in the control group (Patients negative for Gonorrhea but with Lymphoma). This was statistically significant by a p-value < 2.210-16 with an OR= 0.431 (95% CI 0.381-0.487). The patients were then matched by antibiotic treatment to avoid treatment bias. The incidence of Lymphoma was 1.215% (82 patients out of 6,748) in the Gonorrhea group compared to 2.949% (199 patients out of 6748) in the control group. This was statistically significant by a p-value <5.410-10 with an OR= 0.468 (95% CI 0.367-0.596). The study shows a statistically significant correlation between Gonorrhea and a reduced incidence of Lymphoma. Further evaluation is recommended to assess the potential of Gonorrhea in reducing Lymphoma.

Keywords: gonorrhea, lymphoma, STDs, cancer, ICD

Procedia PDF Downloads 173

Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

Procedia PDF Downloads 195

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

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Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

Procedia PDF Downloads 385

Authors: Hossein Rassi, Marjan Moradi Fard, Masoud Houshmand

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Background: Cervical cancer is multistep disease that is thought to result from an interaction between genetic background and environmental factors. Human papillomavirus (HPV) infection is the leading risk factor for cervical intraepithelial neoplasia (CIN) and cervical cancer. In other hand, some of p53 and miRNA polymorphism may plays an important role in carcinogenesis. This study attempts to clarify the relation of p53 genotypes and miR-146a rs2910164 polymorphism in cervical lesions. Method: Forty two archival samples with cervical lesion retired from Khatam hospital and 40 sample from healthy persons used as control group. A simple and rapid method was used to detect the simultaneous amplification of the HPV consensus L1 region and HPV-16,-18, -11, -31, 33 and -35 along with the b-globin gene as an internal control. We use Multiplex PCR for detection of P53 and miR-146a rs2910164 genotypes in our lab. Finally, data analysis was performed using the 7 version of the Epi Info(TM) 2012 software and test chi-square(x2) for trend. Results: Cervix lesions were collected from 42 patients with Squamous metaplasia, cervical intraepithelial neoplasia, and cervical carcinoma. Successful DNA extraction was assessed by PCR amplification of b-actin gene (99bp). According to the results, p53 GG genotype and miR-146a rs2910164 CC genotype was significantly associated with increased risk of cervical lesions in the study population. In this study, we detected 13 HPV 18 from 42 cervical cancer. Conclusion: The connection between several SNP polymorphism and human virus papilloma in rare researches were seen. The reason of these differences in researches' findings can result in different kinds of races and geographic situations and also differences in life grooves in every region. The present study provided preliminary evidence that a p53 GG genotype and miR-146a rs2910164 CC genotype may effect cervical cancer risk in the study population, interacting synergistically with HPV 18 genotype. Our results demonstrate that the testing of p53 codon 72 polymorphism genotypes and miR-146a rs2910164 polymorphism genotypes in combination with HPV18 can serve as major risk factors in the early identification of cervical cancers. Furthermore, the results indicate the possibility of primary prevention of cervical cancer by vaccination against HPV18 in Iran.

Keywords: cervical cancer, HPV18, p53 codon 72 polymorphism, miR-146a rs2910164 polymorphism

Procedia PDF Downloads 434

Authors: Nosakhare Enoma, Alphose Zingoni

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The requirements for various toroidal shell forms are increasing due to new applications, available storage space and the consideration of appearance. Because of the complexity of some of these structural forms, the finite element method is nowadays mainly used for their analysis, even for simple static studies. This paper presents an easy-to-use analytical algorithm for pressurized multi-segmented toroidal shells of revolution. The membrane solution, which acts as a particular solution of the bending-theory equations, is developed based on membrane theory of shells, and a general approach is formulated for quantifying discontinuity effects at the shell junctions using the well-known Geckeler’s approximation. On superimposing these effects, and applying the ensuing solution to the problem of the pressurized toroid with four segments, closed-form stress results are obtained for the entire toroid. A numerical example is carried out using the developed method. The analytical results obtained show excellent agreement with those from the finite element method, indicating that the proposed method can be also used for complementing and verifying FEM results, and providing insights on other related problems.

Keywords: bending theory of shells, membrane hypothesis, pressurized toroid, segmented toroidal vessel, shell analysis

Procedia PDF Downloads 291

Authors: Vesna Lukovic

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Online platforms as new business models play an important role in today’s economy and the functioning of the EU’s internal market. In the travel industry, algorithms used by online platforms for short-stay accommodation provide suggestions and price information to travelers. Those suggestions and recommendations are displayed in search results via recommendation (ranking) systems. There has been a growing consensus that the current legal framework was not sufficient to resolve problems arising from platform practices. In order to enhance the potential of the EU’s Single Market, smaller businesses should be protected, and their rights strengthened vis-à-vis large online platforms. The Regulation (EU) 2019/1150 of the European Parliament and of the Council on promoting fairness and transparency for business users of online intermediation services aims to level the playing field in that respect. This research looks at Airbnb through the lenses of this regulation. The research explores key determinants and finds that although regulation is an important step in the right direction, it is not enough. It does not entail sufficient clarity obligations that would make online platforms an intermediary service which both accommodation providers and travelers could use with ease.

Keywords: algorithm, online platforms, ranking, consumers, EU regulation

Procedia PDF Downloads 108

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

Procedia PDF Downloads 53

Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

Procedia PDF Downloads 103