Search results for: drug property prediction

Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Prakash Singh

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Schools throughout the world are facing increasing challenges in dealing with school violence and drug abuse by pupils. Therefore, the question of the fear of failure to meet the aims and objectives of education inevitably surfaces as it places increasing and challenging demands on educators and all other stakeholders to address this malaise. Multiple studies on the construct tobephobia (TBP) simply define TBP as the fear of failure in education. This study is a continuation of the exploratory studies on the manifestation of fear in education. The primary purpose of this study was to establish how TBP, caused by school violence and drug abuse affects teaching and learning in our schools. The qualitative research method was used for this study. Teachers admitted that they fear for their safety at school. Working in a fearful situation places a high rate of stress and anxiety on them. Tobephobic educators spend most of their time worrying about their fear of violence and drug abuse by pupils and are too frightened to carry out their normal duties. They prefer to stay in familiar surroundings for fear of being attacked by inebriated learners. This study, therefore, contributes to our understanding of the effects of TBP in our schools caused by school violence and drug abuse. Also, this study supplements the evidence accumulated over the past fifteen years that TBP is not a figment of someone’s imagination; it is a gruesome reality affecting the very foundation of our educational system globally to provide quality and equal education to all our learners in a harmonious, collegial school environment.

Keywords: tobephobia, tobephobic educators, fear of failure in education, school violence, drug abuse

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Authors: Jyothi Nagraj, Sudeshna Mukherjee, Rajdeep Chowdhury

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Autophagy has recently been linked with cancer cell survival post drug insult contributing to acquisition of resistance. However, the molecular signaling governing autophagic survival response is poorly explored. In our study, in osteosarcoma (OS) cells cisplatin shock was found to activate both MAPK and autophagy signaling. An activation of JNK and autophagy acted as pro-survival strategy, while ERK1/2 triggered apoptotic signals upon cisplatin stress. An increased sensitivity of the cells to cisplatin was obtained with simultaneous inhibition of both autophagy and JNK pathway. Furthermore, we observed that the autophagic stimulation upon drug stress regulates other developmentally active signaling pathways like the Hippo pathway in OS cells. Cisplatin resistant cells were thereafter developed by repetitive drug exposure followed by clonal selection. Basal levels of autophagy were found to be high in resistant cells to. However, the signaling mechanism leading to autophagic up-regulation and its regulatory effect differed in OS cells upon attaining drug resistance. Our results provide valuable clues to regulatory dynamics of autophagy that can be considered for development of improved therapeutic strategy against resistant type cancers.

Keywords: JNK, autophagy, drug resistance, cancer

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Authors: Maryam Vaezjalali, Koroush Rahimian, Maryam Asli, Tahmineh Kandelouei, Foad Davoodbeglou, Amir H. Kashi

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Objectives: The present study was conducted to assess the HBV molecular profiles including genotypes, subgenotypes, subtypes & mutations in hepatitis B genes. Materials/Patients and Methods: This study was conducted on 229 intravenous drug users who referred to three Drop- in-Centers and a hospital in Tehran. HBV DNA was extracted from HBsAg positive serum samples and amplified by Nested PCR. HBV genotype, subgenotypes, subtype and genes mutation were determined by direct sequencing. Phylogenetic tree was constructed using neighbor- joining (NJ) method. Statistical analyses were carried out by SPSS 20. Results: HBV DNA was found in 3 HBsAg positive cases. Phylogenetic tree of derived HBV DNAs showed the existence of genotype D (subgenotype D1, subtype ayw2). Also immune escape mutations were determined in S gene. Conclusion: There were a few variations and genotypes and subtypes among infected intravenous drug users. This study showed the predominance of genotype D among intravenous drug users. Our study concurs with other reports from Iran, that all showing currently only genotype D is the only detectable genotype in Iran.

Keywords: drug users, genotype, HBV, phylogenetic tree

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Authors: Aneeq Sarwar

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This study assesses the interaction between environmental and intellectual property policy on the rate of invention of environmental inventions and specifically tests for whether there is a synergy between stricter IP regimes and stronger environmental policies. The empirical analysis uses firm and industry-level data from seven OECD countries from 2009 to 2015. We also introduce a new measure of environmental inventions using a Natural Language Processing Topic Modelling technique. We find that intellectual property policy strictness demonstrates greater effectiveness in encouraging inventiveness in environmental inventions when used in combination with stronger environmental policies. This study contributes to existing literature in two ways. First, it devises a method for better identification of environmental technologies, we demonstrate how our method is more comprehensive than existing methods as we are better able to identify not only environmental inventions, but also major components of said inventions. Second, we test how various policy regimes affect the development of environmental technologies, we are the first study to examine the interaction of the environmental and intellectual property policy on firm level innovation.

Keywords: environmental economics, economics of innovation, environmental policy, firm level

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Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

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In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, cross-correlation, avalanches, prediction method

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Authors: Ragaa T. Darwish, Maha A. Demellawy, Haidy M. Megahed, Doreen N. Younan, Wael S. Kholeif

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The testing of drug abuse is authentic in order to affirm the misuse of drugs. Several analytical approaches have been developed for the detection of drugs of abuse in pharmaceutical and common biological samples, but few methodologies have been created to identify them from fingerprints. Liquid Chromatography-Mass Spectrometry (LC-MS) plays a major role in this field. The current study aimed at assessing the possibility of detection of some drugs of abuse (tramadol, clonazepam, and phenobarbital) from fingerprints using LC-MS in drug abusers. The aim was extended in order to assess the possibility of detection of the above-mentioned drugs in fingerprints of drug handlers till three days of handling the drugs. The study was conducted on randomly selected adult individuals who were either drug abusers seeking treatment at centers of drug dependence in Alexandria, Egypt or normal volunteers who were asked to handle the different studied drugs (drug handlers). An informed consent was obtained from all individuals. Participants were classified into 3 groups; control group that consisted of 50 normal individuals (neither abusing nor handling drugs), drug abuser group that consisted of 30 individuals who abused tramadol, clonazepam or phenobarbital (10 individuals for each drug) and drug handler group that consisted of 50 individuals who were touching either the powder of drugs of abuse: tramadol, clonazepam or phenobarbital (10 individuals for each drug) or the powder of the control substances which were of similar appearance (white powder) and that might be used in the adulteration of drugs of abuse: acetyl salicylic acid and acetaminophen (10 individuals for each drug). Samples were taken from the handler individuals for three consecutive days for the same individual. The diagnosis of drug abusers was based on the current Diagnostic and Statistical Manual of Mental disorders (DSM-V) and urine screening tests using immunoassay technique. Preliminary drug screening tests of urine samples were also done for drug handlers and the control groups to indicate the presence or absence of the studied drugs of abuse. Fingerprints of all participants were then taken on a filter paper previously soaked with methanol to be analyzed by LC-MS using SCIEX Triple Quad or QTRAP 5500 System. The concentration of drugs in each sample was calculated using the regression equations between concentration in ng/ml and peak area of each reference standard. All fingerprint samples from drug abusers showed positive results with LC-MS for the tested drugs, while all samples from the control individuals showed negative results. A significant difference was noted between the concentration of the drugs and the duration of abuse. Tramadol, clonazepam, and phenobarbital were also successfully detected from fingerprints of drug handlers till 3 days of handling the drugs. The mean concentration of the chosen drugs of abuse among the handlers group decreased when the days of samples intake increased.

Keywords: drugs of abuse, fingerprints, liquid chromatography–mass spectrometry, tramadol

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Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Delband Yekta Moazami

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Background: For proper care and treatment of HIV patients and drug users, the medical staff and physicians must have a correct and positive attitude and knowledge towards such patients. We aimed to assess the attitudes in a sample of health care workers (HCW) working in different hospitals and clinics and medical students in Georgia towards HIV infected people and drug users in Tbilisi. Method: We conducted a cross-sectional study to assess attitudes of health care workers towards people living with HIV and drug users in hospitals and clinics in Tbilisi. The study was carried out from 1st of May 2020 till 30th of September 2020. Data were collected using a self-administered structured online questionnaire. With this tool we evaluated four facets of attitudes: Discrimination, Acceptance of HIV/AIDS patients, Acceptance of drug users and Fear. All data were imported and analyzed with the software SPSS 22 for windows. Results: In total data was collected from168 respondents, that among them 107 (65%) were women and majority of the participants were medical doctors. Women had more acceptance attitudes rather than men towards drug abusers. We found significant differences regarding expressing negative attitudes among HCW who were more than 50 years old comparing with other age groups in all four aspects. Medical doctors expressed more acceptances towards people with HIV and drug users comparing two other groups. Also our study revealed that the group with working experience 21 years and more, showed more discriminatory attitudes comparing other groups. Conclusion: Based on our study findings, there are significant differences regarding respondent’s attitudes based on gender, medical specialty and working experience in health care system. People struggling with HIV and drug use need nonjudgmental and positive behaviors from health care workers and physicians in order to help them for harm reduction and receiving appropriate treatment.

Keywords: hiv, addiction, attitudes, healthcare workers

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Authors: M. G. Cattaneo

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The non-effectiveness of certain codified human rights is particularly apparent with reference to the lack of access to essential drugs in developing countries, which represents a breach of the human right to receive adequate health assistance. This paper underlines the conflict and the legal contradictions between human rights, namely health rights, international Intellectual Property Rights, in particular patent law, as well as international trade law. The paper discusses the crucial links between R&D costs for innovation, patents and new medical drugs, with the goal of reformulating the hierarchies of priorities and of interests at stake in the international intellectual property (IP) law system. Different from what happens today, International patent law should be a legal instrument apt at rebalancing an axiological asymmetry between the (conflicting) needs at stake The core argument in the paper is the proposal of an alternative pathway, namely a feasible proposal for a patent law reform. IP laws tend to balance the benefits deriving from innovation with the costs of the provided monopoly, but since developing countries and industrialized countries are in completely different political and economic situations, it is necessary to (re)modulate such exchange according to the different needs. Based on this critical analysis, the paper puts forward a proposal, called Trading Time for Space (TTS), whereby a longer time for patent exclusive life in western countries (Time) is offered to the patent holder company, in exchange for the latter selling the medical drug at cost price in developing countries (Space). Accordingly, pharmaceutical companies should sell drugs in developing countries at the cost price, or alternatively grant a free license for the sale in such countries, without any royalties or fees. However, such social service shall be duly compensated. Therefore, the consideration for such a service shall be an extension of the temporal duration of the patent’s exclusive in the country of origin that will compensate the reduced profits caused by the supply at the price cost in developing countries.

Keywords: global health, global justice, patent law reform, access to drugs

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Bin Li, Mei Liu

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Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours.

Keywords: bus transit, arrival time prediction, link-based, path-based

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Authors: Paulinus W. Ihuah, Iyenemi Ibimina Kakulu, Victor A. Akujuru

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The development of the residential property is very expensive in Nigeria, especially as it is observed in Port Harcourt, although it is also investment costly in the other cities of Nigeria. The costly development nature incidentally reasons to the high deficits in residential property availability and affordability for the low-income people. Therefore, the main purpose of this paper is to provide sustainable accommodation design, which should improve residential property expensiveness and shortages for the low-income people. This is achieved through investigation of the tangible requirements and needs of the end-user of the property (low-income people), which thereafter would enhance sustainable and affordable residential property accommodation design for the end-users. Both the quantitative and qualitative instruments of data collection were utilised. The quantitative instrument via questionnaires was designed to examine the real needs and r requirement of the low-income people. However, the qualitative instrument via structured interview was espoused for the gathering of professionals’ opinions on the three predicted sustainable accommodation design alternatives. The analysis employed content analysis parameters, which offered a sustainable accommodation design and designed alternatives minimises costs and environmental impacts whereas exploiting the social satisfaction in residential accommodation developments. The finding underscores that sustainable accommodation design and development is practicable in Nigeria, so that cost of residential accommodation provided through this system is cheap to the low-income people. Further, erection of multi-storey residential accommodation units such as bedsit structure by utilising the concrete frame structure and building the internal and external walls with hollow concrete blocks within areas 60-130 square meters is encouraged. This paper philosophy indicates that by using sustainable accommodation design practices in Nigeria, improvements in the costs and shortages of residential accommodation can be attained for low-income people. However, policies support the government cannot be overemphasised for proper implementation of the suggested scheme.

Keywords: sustainable accommodation, housing design, residential property, low-income people

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Chung-Chih Lin, Chien-Liang Wu

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The detection of the polymer melt state during manufacture process is regarded as an efficient way to control the molded part quality in advance. Online monitoring rheological property of polymer melt during processing procedure provides an approach to understand the melt state immediately. Rheological property reflects the polymer melt state at different processing parameters and is very important in injection molding process especially. An approach that demonstrates how to calculate rheological property of polymer melt through in-process measurement, using injection molding as an example, is proposed in this study. The system consists of two sensors and a data acquisition module can process the measured data, which are used for the calculation of rheological properties of polymer melt. The rheological properties of polymer melt discussed in this study include shear rate and viscosity which are investigated with respect to injection speed and melt temperature. The results show that the effect of injection speed on the rheological properties is apparent, especially for high melt temperature and should be considered for precision molding process.

Keywords: injection molding, melt viscosity, shear rate, monitoring

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Authors: Vadim Makarov, Stewart T.Cole

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PBTZ169 is novel drug candidate with high efficacy in animals models, and its combination treatment of PBTZ169 with BDQ and pyrazinamide was shown to be more efficacious than the standard treatment for tuberculosis in a mouse model. The target of PBTZ169 is famous DprE1, an essential enzyme in cell wall biosynthesis. The crystal structure of the DprE1-PBTZ169 complex reveals formation of a semimercaptal adduct with Cys387 in the active site and explains the irreversible inactivation of the enzyme. Furthermore, this drug candidate demonstrated during preclinical research ‘drug like’ properties what made it an attractive drug candidate to treat tuberculosis in humans. During first clinical trials several cohorts of the healthy volunteers were treated by the single doses of PBTZ169 as well as two weeks repeated treatment was chosen for two maximal doses. As expected PBTZ169 was well tolerated, and no significant toxicity effects were observed during the trials. The study of the metabolism shown that human metabolism of PBTZ169 is very different from microbial or animals compound transformation. So main pathway of microbial, mice and less rats metabolism connected with reduction processes, but human metabolism mainly connected with oxidation processes. Due to this difference we observed several metabolites of PBTZ169 in humans with antitubercular activity, and now we can conclude that animal antituberculosis activity of PBTZ169 is a result not only activity of the drug itself, but it is a result of the sum activity of the drug and its metabolites. Direct antimicrobial plasma activity was studied, and such activity was observed for 24 hours after human treatment for some doses. This data gets high chance for good efficacy of PBTZ169 in human for treatment TB infection. Second phase of clinical trials was started summer of 2017 and continues to the present day. Available data will be presented.

Keywords: clinical trials, DprE1, PBTZ169, metabolism

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Authors: A. Hilsana, Vinay U. Rao, M. Sudhakar

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Even though a number of non-steroidal anti-inflammatory drugs (NSAIDS) are available with different chemistries, they share a common solubility characteristic that is they are relatively more soluble in alkaline environment and practically insoluble in acidic environment. This work deals with developing a wax matrix drug delivery platform for controlled delivery of three model NSAIDS, Diclofenac sodium (DNa), Mefenamic acid (MA) and Naproxen (NPX) using the melt granulation technique. The aim of developing the platform was to have a general understanding on how an erodible matrix system modulates drug delivery rate and extent and how it can be optimized to give a delivery system which shall release the drug as per a common target product profile (TPP). Commonly used waxes like Cetostearyl alcohol and stearic acid were used singly an in combination to achieve a TPP of not 15 to 35% in 1 hour and not less than 80% Q in 24 hours. Full factorial design of experiments was followed for optimization of the formulation.

Keywords: NSAIDs, controlled delivery, target product profile, melt granulation

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Friday G. Okibe, Edit B. Agbaji, Victor O. Ajibola, Christain C. Onoyima

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Controlled drug delivery systems reduce dose-dependent toxicity associated with potent drugs, including anticancer drugs. In this research, hydroxyapatite (HA) and hydroxyapatite-sodium alginate nanocomposites (HASA) were successfully prepared and characterized using Fourier Transform Infrared spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). The FTIR result showed absorption peaks characteristics of pure hydroxyapatite (HA), and also confirmed the chemical interaction between hydroxyapatite and sodium alginate in the formation of the composite. Image analysis from SEM revealed nano-sized hydroxyapatite and hydroxyapatite-sodium alginate nanocomposites with irregular morphologies. Particle size increased with the formation of the nanocomposites relative to pure hydroxyapatite, with no significant change in particles morphologies. Drug loading and in-vitro drug release study were carried out using synthetic body fluid as the release medium, at pH 7.4 and 37 °C and under perfect sink conditions. The result shows that drug loading is highest for pure hydroxyapatite and decreased with increasing quantity of sodium alginate. However, the release study revealed that HASA-5%wt and HASA-20%wt presented better release profile than pure hydroxyapatite, while HASA-33%wt and HASA-50%wt have poor release profiles. This shows that Methotrexate-loaded hydroxyapatite-sodium alginate if prepared under optimal conditions is a potential carrier for effective delivery of Methotrexate.

Keywords: drug-delivery, hydroxyapatite, methotrexate, nanocomposites, sodium alginate

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Authors: Gajera Lalit, Shah Pranav, Shah Shailesh

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Venlafaxine hydrochloride has a short elimination half-life of 5 ± 2 hr, and absorption window in the upper part of gastrointestinal tract. The conventional tablets need to be administered two to three times a day and possess an oral bioavailability of 45%. The purpose of this study was to formulate gastroretentive effervescent floating tablets of Venlafaxine HCl. Different grades of HPMC namely K15M, K4M, K100M and E15LV were employed as swelling polymers whereas sodium bicarbonate was employed as gas generating agent. The direct compression method was employed for the formulation of tablets. The tablets were evaluated in terms of hardness, friability, weight variation, drug content, water uptake, in-vitro floating behavior and in-vitro drug release study. All the formulations exhibited very short floating lag time of < 1 min and total floating time of 12 hr. Formulation L3 containing 25 mg and 75 mg of HPMC E15 LV and HPMC K15M respectively exhibited complete drug release within 12 hrs.

Keywords: venlafaxine HCl, hydroxyl propyl methylcellulose, floating gastro retentive tablets, in-vitro drug release, non-fickian diffusion

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Authors: Eyosiyas Aga

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The development of information communication technology has transformed the paper-based mapping and land registration processes to a computerized and networked system. The computerization and networking of real property information system play a vital role in good governance and sustainable development of emerging countries through cost effective, easy and accessible service delivery for the customer. The efficient, transparent and sustainable real property system is becoming the basic infrastructure for the urban development thus improve the data management system and service delivery in the organizations. In Ethiopia, the real property administration is paper based as a result, it confronted problems of data management, illegal transactions, corruptions, and poor service delivery. In order to solve this problem and to facilitate real property market, the implementation of web-based real property updating system is crucial. A web-based real property updating is one of the automation (computerizations) methods to facilitate data sharing, reduce time and cost of the service delivery in real property administration system. In additions, it is useful for the integration of data onto different information systems and organizations. This system is designed by combining open source software which supported by open Geo-spatial consortium. The web-based system is mainly designed by using open source software with the help of open Geo-spatial Consortium. The Open Geo-spatial Consortium standards such as the Web Feature Service and Web Map Services are the most widely used standards to support and improves web-based real property updating. These features allow the integration of data from different sources, and it can be used to maintain consistency of data throughout transactions. The PostgreSQL and Geoserver are used to manage and connect a real property data to the flex viewer and user interface. The system is designed for both internal updating system (municipality); which is mainly updating of spatial and textual information, and the external system (customer) which focus on providing and interacting with the customer. This research assessed the potential of open source web applications and adopted this technology for real property updating system in Ethiopia through simple, cost effective and secured way. The system is designed by combining and customizing open source software to enhance the efficiency of the system in cost effective way. The existing workflow for real property updating is analyzed to identify the bottlenecks, and the new workflow is designed for the system. The requirement is identified through questionnaire and literature review, and the system is prototype for the study area. The research mainly aimed to integrate human resource with technology in designing of the system to reduce data inconsistency and security problems. In additions, the research reflects on the current situation of real property administration and contributions of effective data management system for efficient, transparent and sustainable urban development in Ethiopia.

Keywords: cadaster, real property, sustainable, transparency, web feature service, web map service

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Authors: Yasmin Begum Mohammed

Abstract:

Ketorolac tromethamine (KTM) is a non-steroidal anti-inflammatory drug with a potent analgesic and anti-inflammatory activity due to prostaglandin related inhibitory effect of drug. It is a non-selective cyclo-oxygenase inhibitor. The drug is currently used orally and intramuscularly in multiple divided doses, clinically for the management arthritis, cancer pain, post-surgical pain, and in the treatment of migraine pain. KTM has short biological half-life of 4 to 6 hours, which necessitates frequent dosing to retain the action. The frequent occurrence of gastrointestinal bleeding, perforation, peptic ulceration, and renal failure lead to the development of other drug delivery strategies for the appropriate delivery of KTM. The ideal solution would be to target the drug only to the cells or tissues affected by the disease. Drug targeting could be achieved effectively by liposomes that are biocompatible and biodegradable. The aim of the study was to develop a parenteral liposome formulation of KTM with improved efficacy while reducing side effects by targeting the inflammation due to arthritis. PEG-anchored (stealth) and non-PEG-anchored liposomes were prepared by thin film hydration technique followed by extrusion cycle and characterized for in vitro and in vivo. Stealth liposomes (SLs) exhibited increase in percent encapsulation efficiency (94%) and 52% percent of drug retention during release studies in 24 h with good stability for a period of 1 month at -20°C and 4°C. SLs showed about maximum 55% of edema inhibition with significant analgesic effect. SLs produced marked differences over those of non-SL formulations with an increase in area under plasma concentration time curve, t₁/₂, mean residence time, and reduced clearance. 0.3% of the drug was detected in arthritic induced paw with significantly reduced drug localization in liver, spleen, and kidney for SLs when compared to other conventional liposomes. Thus SLs help to increase the therapeutic efficacy of KTM by increasing the targeting potential at the inflammatory region.

Keywords: biodistribution, ketorolac tromethamine, stealth liposomes, thin film hydration technique

Procedia PDF Downloads 271

Authors: Pallavi Mane, Nikita Mankar, Shraddha Mazire, Rasika Pashankar

Abstract:

Content sharing sites are very useful in sharing information and images. However, with the increasing demand of content sharing sites privacy and security concern have also increased. There is need to develop a tool for controlling user access to their shared content. Therefore, we are developing an Adaptive Privacy Policy Prediction (A3P) system which is helpful for users to create privacy settings for their images. We propose the two-level framework which assigns the best available privacy policy for the users images according to users available histories on the site.

Keywords: online information services, prediction, security and protection, web based services

Procedia PDF Downloads 335

Authors: Sam Khozama, Ali M. Mayya

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 133

Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

Abstract:

India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

Procedia PDF Downloads 276

Authors: Ahmad Saleem

Abstract:

Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

Procedia PDF Downloads 70

Authors: Farzad Khajavi, Farzaneh Jalilfar, Faranak Jafari, Leila Shokrani

Abstract:

Formulation of Ranolazine in the form of extended-release tablet in 500 mg dosage form was performed using Eudragit L100-55 as a retarding agent. Drug-release profiles were investigated in comparison with the reference Ranexa extended-release 500 mg tablet. F₂ and f₁ were calculated as 64.16 and 8.53, respectively. According to Peppas equation, the release of drug is controlled by diffusion (n=0.5). The tablets were put into accelerated stability conditions (40 °C, 75% humidity) for 3 and 6 months. The dissolution release profiles and other physical and chemical characteristics of the tablets confirmed the robustness and stability of formulation in this condition.

Keywords: drug release, extended-release tablet, ranolazine, stability

Procedia PDF Downloads 126

Authors: Alireza Jalalvand, Maryam Saleh, Somayeh Behjat Khatouni, Zahra Bahri Najafi, Foroozan Fatahinia, Narges Ismailzadeh, Behrokh Farahmand

Abstract:

Object: In the last two decades, the recent outbreak of Coronavirus (SARS-CoV-2) imposed a global pandemic in the world. Despite the increasing prevalence of the disease, there are no effective drugs to treat it. A suitable and rapid way to afford an effective drug and treat the global pandemic is a computational drug study. This study used molecular docking methods to examine the potential inhibition of over 50 antiviral drugs against three fundamental proteins of SARS-CoV-2. METHODS: Through a literature review, three important proteins (a key protease, RNA-dependent RNA polymerase (RdRp), and spike) were selected as drug targets. Three-dimensional (3D) structures of protease, spike, and RdRP proteins were obtained from the Protein Data Bank. Protein had minimal energy. Over 50 antiviral drugs were considered candidates for protein inhibition and their 3D structures were obtained from drug banks. The Autodock 4.2 software was used to define the molecular docking settings and run the algorithm. RESULTS: Five drugs, including indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, exhibited the highest inhibitory potency against all three proteins based on the binding energies and drug binding positions deduced from docking and hydrogen-bonding analysis. Conclusions: According to the results, among the drugs mentioned, saquinavir and lopinavir showed the highest inhibitory potency against all three proteins compared to other drugs. It may enter laboratory phase studies as a dual-drug treatment to inhibit SARS-CoV-2.

Keywords: covid-19, drug repositioning, molecular docking, lopinavir, saquinavir

Procedia PDF Downloads 59