Search results for: retroreflective marker data

Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

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Authors: Sandeep Pandey, Nimra Habib, Ranjana Singh, Abbas Ali Mahdi

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Leukemia is a malignancy of blood that mainly affects children and young adults; while advancement in the early diagnosis will have the potential to improve the outcome of diseases. A wide range of disease including leukemia shows inflammatory signals in their pathogenesis. In a pilot study conducted in our laboratory, 52 people were screened, of which 26 had leukemia and 26 were free from any kind of malignancy. We performed the estimation of the inflammatory cytokine Interleukin-8 and it was found significantly raised in all the leukemia patients concerning healthy volunteers who participated in the study. Flow cytometry had been performed for the confirmation of leukemia and further genomic, and proteomic, analyses of the sample revealed that IL-8 levels showed a positive correlation in patients with leukemia. The results had shown constitutive secretion of interleukin-8 by leukemia cells. So, our finding demonstrated that IL-8 is considered to have a role in the pathogenesis of leukemia, and quantification of IL-8 levels in leukemia conditions might be more useful and feasible in the clinical setting for the prediction of drug responses where it may represent a putative target for innovative diagnostic toward effective therapeutic approaches. However, further research explorations in this area are needed that include a greater number of patients with all different forms of leukemia, and estimating their IL-8 levels may hold the key for the additional predictive values on the recurrence of leukemia and its prognosis.

Keywords: T-ALL, IL-8, leukemia pathogenesis, cancer therapeutics

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

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Authors: Vichayada Laohapiboolkul

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Introduction: As the legal use of cannabis is being widely accepted throughout the world, medical cannabis has been explored in order to become an alternative cure for patients. Tetrahydrocannabinol (THC) and Cannabidiol (CBD) are natural cannabinoids found in the Cannabis plant which is proved to have positive treatment for various diseases and symptoms such as chronic pain, neuropathic pain, spasticity resulting from multiple sclerosis, reduce cancer-associated pain, autism spectrum disorders (ASD), dementia, cannabis and opioid dependence, psychoses/schizophrenia, general social anxiety, posttraumatic stress disorder, anorexia nervosa, attention-deficit hyperactivity disorder, and Tourette's disorder. Regardless of all the medical benefits, THC, if not metabolized, can lead to mild up to severe adverse drug reactions (ADR). The enzyme CYP2C19 was found to be one of the metabolizers of THC. However, the suballele CYP2C19*2 manifests as a poor metabolizer which could lead to higher levels of THC than usual, possibly leading to various ADRs. Objective: The aim of this study was to investigate the distribution of CYP2C19, specifically CYP2C19*2, genes in Thai patients treated with medical cannabis along with adverse drug reactions. Materials and Methods: Clinical data and EDTA whole blood for DNA extraction and genotyping were collected from patients for this study. CYP2C19*2 (681G>A, rs4244285) genotyping was conducted using the Real-time PCR (ABI, Foster City, CA, USA). Results: There were 42 medical cannabis-induced ADRs cases and 18 medical cannabis tolerance controls who were included in this study. A total of 60 patients were observed where 38 (63.3%) patients were female and 22 (36.7%) were male, with a range of age approximately 19 - 87 years. The most apparent ADRs for medical cannabis treatment were dry mouth/dry throat (76.7%), followed by tachycardia (70%), nausea (30%) and a few arrhythmias (10%). In the total of 27 cases, we found a frequency of 18 CYP2C19*1/*1 alleles (normal metabolizers, 66.7%), 8 CYP2C19*1/*2 alleles (intermediate metabolizers, 29.6%) and 1 CYP2C19*2/*2 alleles (poor metabolizers, 3.7%). Meanwhile, 63.6% of CYP2C19*1/*1, 36.3% and 0% of CYP2C19*1/*2 and *2/*2 in the tolerance controls group, respectively. Conclusions: This is the first study to confirm the distribution of CYP2C19*2 allele and the prevalence of poor metabolizer genes in Thai patients who received medical cannabis for treatment. Thus, CYP2C19 allele might serve as a pharmacogenetics marker for screening before initiating treatment.

Keywords: medical cannabis, adverse drug reactions, CYP2C19, tetrahydrocannabinol, poor metabolizer

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

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Authors: Sven Gehrke, Johannes Ruhland

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Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

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Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

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Authors: Amornnat Thuppia, Pornrut Rabintossaporn, Suphaket Saenthaweesuk, Nuntiya Somparn

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The aim of this present study was to determine the antioxidant effects and its mechanism of aqueous leaves extract of Bauhinia acuminata (BA) in rat. The extract was screened for its phytochemical contents and antioxidant activity in vitro. Moreover, the extract was studied in rats to evaluate its effects in vivo. Rats were orally administered with the extract at the dose of 50, 100 and 200 mg/kg for 28 days. Phytochemical screening of plant extracts showed the presence of saponin, alkaloid, cardiac glycosides, flavonoids, tannin and steroid compounds. The extract contained phenolic compounds 53.36 ± 1.01 mg of gallic acid equivalents per gram BA extract. The free radical scavenging activity assessed by DPPH assay gave IC50 of 44.47 ± 2.83 µg/mL, which is relatively lower than that of BHT with IC50 of 12.34 ± 1.14µg/mL. In the animals, the extract was well tolerated by the animals throughout the 28 days of study as shown by normal serum levels AST, ALP, ALT, BUN and Cr as well as normal histology of liver and pancreatic and kidney tissue. Significantly, reduction of serum oxidative stress markers malondialdehyde (MDA) was found in rat treated with BA extract compared with control. Taken together, this study provides evidence that Bauhinia acuminata (BA) exhibits direct antioxidant properties and induces cytoprotective enzyme in vivo.

Keywords: Bauhinia acuminata, antioxidant, malondialdehyde (MDA), oxidative marker

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Authors: S. Koppikar, P. Kulkarni, D. Ingale , N. Wagh, S. Deshpande, A. Mahajan, A. Harsulkar

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Crucial role of reactive oxygen species (ROS) in the progression Osteoarthritis (OA) pathogenesis has been endorsed several times though its exact mechanism remains unclear. Oxidative stress is known to instigate classical stress factors such as cytokines, chemokines and ROS, which hampers cartilage remodelling process and ultimately results in worsening the disease. Synovial fluid (SF) is a biological communicator between cartilage and synovium that accumulates redox and biochemical signalling mediators. The present work attempts to measure several oxidative stress markers in the synovial fluid obtained from knee OA patients with varying degree of disease severity. Thirty OA and five Meniscal-tear (MT) patients were graded using Kellgren-Lawrence scale and assessed for Nitric oxide (NO), Nitrate-Nitrite (NN), 2,2-diphenyl-1-picrylhydrazyl (DPPH), Ferric Reducing Antioxidant Potential (FRAP), Catalase (CAT), Superoxide dismutase (SOD) and Malondialdehyde (MDA) levels for comparison. Out of various oxidative markers studied, NO and SOD showed significant difference between moderate and severe OA (p= 0.007 and p= 0.08, respectively), whereas CAT demonstrated significant difference between MT and mild group (p= 0.07). Interestingly, NN revealed statistically positive correlation with OA severity (p= 0.001 and p= 0.003). MDA, a lipid peroxidation by-product was estimated maximum in early OA when compared to MT (p= 0.06). However, FRAP did not show any correlation with OA severity or MT control. NO is an essential bio-regulatory molecule essential for several physiological processes, and inflammatory conditions. However, due to its short life, exact estimation of NO becomes difficult. NO and its measurable stable products are still it is considered as one of the important biomarker of oxidative damage. Levels of NO and nitrite-nitrate in SF of patients with OA indicated its involvement in the disease progression. When SF groups were compared, a significant correlation among moderate, mild and MT groups was established. To summarize, present data illustrated higher levels of NO, SOD, CAT, DPPH and MDA in early OA in comparison with MT, as a control group. NN had emerged as a prognostic bio marker in knee OA patients, which may act as futuristic targets in OA treatment.

Keywords: antioxidant, knee osteoarthritis, oxidative stress, synovial fluid

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Authors: Romeo Botes, Imelda Smit

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In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

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Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

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In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research.

Keywords: forecasting, generalized extreme value (GEV), meteorology, return level

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Authors: Lavinia Ruta, Ileana Farcasanu

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The yeast surface display (YSD) technique enables the expression of proteins on yeast cell surfaces, facilitating the identification and isolation of proteins with targeted binding properties, such as nanobodies. Nanobodies, derived from camelid species, are single-domain antibody fragments renowned for their high affinity and specificity towards target proteins, making them valuable in research and potentially in therapeutics. Their advantages include a compact size (~15 kDa), robust stability, and the ability to target challenging epitopes. The project endeavors to establish and validate a platform for producing Green Fluorescent Protein (GFP) and mCherry nanobodies using the yeast surface display method. mCherry, a prevalent red fluorescent protein sourced from coral species, is commonly utilized as a genetic marker in biological studies due to its vibrant red fluorescence. The GFP-nanobody, a single variable domain of heavy-chain antibodies (VHH), exhibits specific binding to GFP, offering a potent means for isolating and engineering fluorescent protein fusions across various biological research domains. Both GFP and mCherry nanobodies find specific utility in cellular imaging and protein analysis applications.

Keywords: YSD, nanobodies, GFP, Saccharomyces cerevisiae

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Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

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Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: hit rate, locality of program, stack cache, stack data

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Authors: Wajahat Munir, Muhammad Haseeb, Adeel Anjum, Basit Raza, Ahmad Kamran Malik

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Databases are the key components of the software systems. Due to the exponential growth of data, it is the concern that the data should be accurate and available. The data in databases is vulnerable to internal and external threats, especially when it contains sensitive data like medical or military applications. Whenever the data is changed by malicious intent, data analysis result may lead to disastrous decisions. Autonomic self-healing is molded toward computer system after inspiring from the autonomic system of human body. In order to guarantee the accuracy and availability of data, we propose a technique which on a priority basis, tries to avoid any malicious transaction from execution and in case a malicious transaction affects the system, it heals the system in an isolated mode in such a way that the availability of system would not be compromised. Using this autonomic system, the management cost and time of DBAs can be minimized. In the end, we test our model and present the findings.

Keywords: autonomic computing, self-healing, threat avoidance, security

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Authors: Mohammed R. Elkobaisi, Abdelsalam Maatuk

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The search engines are the large scale information retrieval tools from the Web that are currently freely available to all. This paper explains how to convert the raw resulted number of search engines into useful information. This represents a new method for data gathering comparing with traditional methods. When a query is submitted for a multiple numbers of keywords, this take a long time and effort, hence we develop a user interface program to automatic search by taking multi-keywords at the same time and leave this program to collect wanted data automatically. The collected raw data is processed using mathematical and statistical theories to eliminate unwanted data and converting it to usable data.

Keywords: search engines, information extraction, agent system

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Authors: Watchawin Ekphinitphithaya, Patompong Satapornpong

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Introduction: Carbamazepine (CBZ) is one of the most prescribed antiepileptic drugs (AEDs) by neurologists and non-neurologist worldwide. CBZ is usually prescribed along with other drugs, leading to the possibility of severe cutaneous adverse drug reactions (SCARs). The HLA-B*15:02 is strongly associated with CBZ-induced Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS–TEN) in the Han Chinese and other Asian populations but not in European populations, while HLA-A*31:01 allele has been reported to be associated with CBZ-induced SCARs in European population and Japanese. Objective: The aim of this study is to investigate the distribution of pharmacogenetics HLA-A*31:01 marker in a healthy Thai population associated with Carbamazepine-induced SCARs. Materials and Methods: Prospective study, 350 unrelated healthy Thais were recruited in this study. Human leukocyte antigen-A alleles were genotyped using PCR-sequence specific oligonucleotides (PCR-SSOs). Results: The frequency of HLA-A alleles were HLA-A*11:01 (190 alleles, 27.14%), HLA-A*24:02 (82 alleles, 11.71%), HLA-A*02:03 (80 alleles, 11.43%), HLA-A*33:03 (76 alleles, 10.86%), HLA-A*02:07 (58 alleles, 8.29%), HLA-A*02:01 (35 alleles, 5.00%), HLA-A*24:07 (29 alleles, 4.14%), HLA-A*02:06 – HLA-A*30:01 (15 alleles, 2.14%), and HLA-A*01:01 (14 alleles, 2.00%). Particularly, the number of HLA-A*31:01 alleles was 6 of 700 (0.86%) in the healthy Thai population. Many research presented varying distributions of HLA-A*31:01 in Asians, including 2% of Han Chinese, 9% of Japanese and 5% of Koreans. In addition, this allele was found approximately 2-5% in the Caucasian population. Conclusions: Thus, the pharmacogenetics database is vital to support in many populations, especially in Thais, for screening HLA-A*31:01 allele to avoid CBZ-induced SCARs before initiating treatments in each population.

Keywords: Carbamazepine, HLA-A*31:01, Thai population, pharmacogenetics

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Authors: S. C. Sharma, Ankit Gambhir, Rajeev Arya

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In today’s era data security is an important concern and most demanding issues because it is essential for people using online banking, e-shopping, reservations etc. The two major techniques that are used for secure communication are Cryptography and Steganography. Cryptographic algorithms scramble the data so that intruder will not able to retrieve it; however steganography covers that data in some cover file so that presence of communication is hidden. This paper presents the implementation of Ron Rivest, Adi Shamir, and Leonard Adleman (RSA) Algorithm with Image and Audio Steganography and Data Encryption Standard (DES) Algorithm with Image and Audio Steganography. The coding for both the algorithms have been done using MATLAB and its observed that these techniques performed better than individual techniques. The risk of unauthorized access is alleviated up to a certain extent by using these techniques. These techniques could be used in Banks, RAW agencies etc, where highly confidential data is transferred. Finally, the comparisons of such two techniques are also given in tabular forms.

Keywords: audio steganography, data security, DES, image steganography, intruder, RSA, steganography

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Authors: Anushtha Saxena

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This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Dražen Cvitanić, Biljana Maljković

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This paper presents models for predicting operating speeds on tangent sections of two-lane rural roads developed on continuous speed data. The data corresponds to 20 drivers of different ages and driving experiences, driving their own cars along an 18 km long section of a state road. The data were first used for determination of maximum operating speeds on tangents and their comparison with speeds in the middle of tangents i.e. speed data used in most of operating speed studies. Analysis of continuous speed data indicated that the spot speed data are not reliable indicators of relevant speeds. After that, operating speed models for tangent sections were developed. There was no significant difference between models developed using speed data in the middle of tangent sections and models developed using maximum operating speeds on tangent sections. All developed models have higher coefficient of determination then models developed on spot speed data. Thus, it can be concluded that the method of measuring has more significant impact on the quality of operating speed model than the location of measurement.

Keywords: operating speed, continuous speed data, tangent sections, spot speed, consistency

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Authors: Vandana Singh, Subash Singh

Abstract:

Introduction: Oral squamous cell carcinoma is the most common malignant tumor of the oral cavity. Normally the death of cell and the growth are active processes and depend not only on external factors but also on the expression of genes like Bcl-2, which activate and inhibit apoptosis. The term Bcl-2 is an acronym for B-cell lymphoma/ leukemia -2 genes. Objectives: An attempt was made to evaluate Bcl-2 oncoprotein expression in patients with oral precancer and cancer and to assess possible correlation between Bcl-2 oncoprotein expression and clinicopathological features of oral precancer and cancer. Material and Methods: This is a selective prospective clinical and immunohistochemical study. Clinicopathological examination is correlated with immunohistochemical findings. The immunolocalization of Bcl-2 protein is performed using the labeled streptavidin biotin (LSAB) method. To visualize the reaction, 3, 3-diaminobenzidine (DAB) is used. Results: Bcl-2 expression was positive in 11 [36.66 %, low Bcl-2 expression 3 (10.00 %), moderate Bcl-2 expression 7 (23.33 %), and high Bcl-2 expression 1 (3.33 %)] oral cancer cases and in 14 [87.50 %, low expression 8 (50 %), moderate expression 6 (37.50 %)] precancer cases. Conclusion: On the basis of the results of our study we conclude that positive Bcl-2 expression may be an indicator of poor prognosis in oral cancer and precancer. Relevance: It has been reported that there is deregulation of Bcl-2 expression during progression from oral epithelial dysplasia to squamous cell carcinoma. It can be used for revealing progression of epithelial dysplasia to malignancy and as a prognostic marker in oral precancer and cancer.

Keywords: BcL-2, immunohistochemistry, oral cancer, oral precancer

Procedia PDF Downloads 252

Authors: S. Nickolas, Shobha K.

Abstract:

The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

Procedia PDF Downloads 264

Authors: Ruixia Liu

Abstract:

Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

Procedia PDF Downloads 224

Authors: Sook Ching Yee, Angela Siew Hoong Lee

Abstract:

Big data technologies have become a trend to exploit business opportunities and provide valuable business insights through the analysis of big data. However, there are still many organizations that have yet to adopt big data technologies especially small and medium organizations (SME). This study uses the technology acceptance model (TAM) to look into several constructs in the TAM and other additional constructs which are positive affect, negative affect, organizational factor and motivational factor. The conceptual model proposed in the study will be tested on the relationship and influence of positive affect, negative affect, organizational factor and motivational factor towards the intention to use big data technologies to produce an outcome. Empirical research is used in this study by conducting a survey to collect data.

Keywords: big data technologies, motivational factor, negative affect, organizational factor, positive affect, technology acceptance model (TAM)

Procedia PDF Downloads 345