Search results for: data driven diagnosis

Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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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: Busiswa Madikazi

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This systematic mapping study explores the underrepresentation of women's contributions to farming in the Global South within the development of Information and Communication Technologies (ICT)-based extension methods. Despite women farmers constituting 70% of the agricultural labour force, their productivity is hindered by various constraints, including illiteracy, household commitments, and limited access to credit and markets. A systematic mapping approach was employed with the aim of identifying evidence gaps in existing ICT extension for women farmers. The data collection protocol follows a structured approach, incorporating key criteria for inclusion, exclusion, search strategy, and coding and the PICO strategy (Population, Intervention, Comparator, and Outcome). The results yielded 119 articles that qualified for inclusion. The findings highlight that mobile phone apps (WhatsApp) and radio/television programming are the primary extension methods employed while integrating ICT with training, field visits, and demonstrations are underutilized. Notably, the study emphasizes the inadequate attention to critical issues such as food security, gender equality, and attracting youth to farming within ICT extension efforts. These findings indicate a significant policy and practice gap, neglecting community-driven approaches that cater to women's specific needs and enhance their agricultural production. Map highlights the importance of refocusing ICT extension efforts to address women farmers’ unique challenges, thereby contributing to their empowerment and improving agricultural practices.

Keywords: agricultural extension, ICT, women farmers, smallholders

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Authors: S. Nickolas, Shobha K.

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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

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Authors: Ruixia Liu

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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

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Authors: Niklas Bondesson

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Purpose: This paper attempts to examine how different facets of attitudinal brand loyalty are determined by different brand image elements in business markets. Design/Methodology/Approach: Statistical analysis is employed to data from a web survey, covering 226 professional packaging buyers in eight countries. Findings: The results reveal that different brand loyalty facets have different antecedents. Affective brand loyalties (or loyalty 'feelings') are mainly driven by customer associations to service relationships, whereas customers’ loyalty intentions (to purchase and recommend a brand) are triggered by associations to the general reputation of the company. The findings also indicate that willingness to pay a price premium is a distinct form of loyalty, with unique determinants. Research implications: Theoretically, the paper suggests that corporate B2B brand loyalty needs to be conceptualised with more refinement than has been done in extant B2B branding work. Methodologically, the paper highlights that single-item approaches can be fruitful when measuring B2B brand loyalty, and that multi-item scales can conceal important nuances in terms of understanding why customers are loyal. Practical implications: The idea of a loyalty 'silver metric' is an attractive idea, but this study indicates that firms who rely too much on one single type of brand loyalty risk to miss important building blocks. Originality/Value/Contribution: The major contribution is a more multi-faceted conceptualisation, and measurement, of corporate B2B brand loyalty and its brand image determinants than extant work has provided.

Keywords: brand equity, business-to-business branding, industrial marketing, buying behaviour

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Authors: Sook Ching Yee, Angela Siew Hoong Lee

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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)

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

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Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.

Keywords: hybrid MAC protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node dataprocessing, Z-MAC

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Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

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The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

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Authors: Abdeljalil Beniiche

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The limitation with smart contracts is that they cannot access external data that might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and its key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.

Keywords: blockchain, oracles, oracles design, human oracles

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Authors: Ruairi J. McMillan

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Introduction: This critical analysis aims to ascertain how well neuroanatomical aetiologies are communicated within 20 case reports of aphasia. Neuroanatomical visualisations based on dissected brain specimens were produced and combined with white matter tract and vascular taxonomies of function in order to address the most consistently underreported features found within the aphasic case study reports. Together, these approaches are intended to integrate aphasiological knowledge from the past 20 years with aphasiological diagnostics, and to act as prototypal resources for both researchers and clinical professionals. The medico-legal precedent for aphasia diagnostics under Canadian, US and UK case law and the neuroimaging/neurological diagnostics relative to the functional capacity of aphasic patients are discussed in relation to the major findings of the literary analysis, neuroimaging protocols in clinical use today, and the neuroanatomical aetiologies of different aphasias. Basic Methodology: Literature searches of relevant scientific databases (e.g, OVID medline) were carried out using search terms such as aphasia case study (year) & stroke induced aphasia case study. A series of 7 diagnostic reporting criteria were formulated, and the resulting case studies were scored / 7 alongside clinical stroke criteria. In order to focus on the diagnostic assessment of the patient’s condition, only the case report proper (not the discussion) was used to quantify results. Statistical testing established if specific reporting criteria were associated with higher overall scores and potentially inferable increases in quality of reporting. Statistical testing of whether criteria scores were associated with an unclear/adjusted diagnosis were also tested, as well as the probability of a given criterion deviating from an expected estimate. Major Findings: The quantitative analysis of neuroanatomically driven diagnostics in case studies of aphasia revealed particularly low scores in the connection of neuroanatomical functions to aphasiological assessment (10%), and in the inclusion of white matter tracts within neuroimaging or assessment diagnostics (30%). Case studies which included clinical mention of white matter tracts within the report itself were distributed among higher scoring cases, as were case studies which (as clinically indicated) related the affected vascular region to the brain parenchyma of the language network. Concluding Statement: These findings indicate that certain neuroanatomical functions are integrated less often within the patient report than others, despite a precedent for well-integrated neuroanatomical aphasiology also being found among the case studies sampled, and despite these functions being clinically essential in diagnostic neuroimaging and aphasiological assessment. Therefore, ultimately the integration and specificity of aetiological neuroanatomy may contribute positively to the capacity and autonomy of aphasic patients as well as their clinicians. The integration of a full aetiological neuroanatomy within the reporting of aphasias may improve patient outcomes and sustain autonomy in the event of medico-ethical investigation.

Keywords: aphasia, language network, functional neuroanatomy, aphasiological diagnostics, medico-legal ethics

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Authors: Frank Fan

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PharmD (Doctor of Pharmacy) education has confronted an increasing challenge — curricular overload, a phenomenon resulting from the expansion of curricular requirements, as PharmD education strives to produce graduates who are practice-ready. The aftermath of the global pandemic has amplified the need for healthcare professionals, leading to a growing trend of assigning more responsibilities to them to address the global healthcare shortage. For instance, the pharmacist’s role has expanded to include not only compounding and distributing medication but also providing clinical services, including minor ailments management, patient counselling and vaccination. Consequently, PharmD programs have responded by continually expanding their curricula adding more requirements. While these changes aim to enhance the education and training of future professionals, they have also led to unintended consequences, including curricular overload, student burnout, and a potential decrease in program quality. To address the issue and ensure program quality, there is a growing need for evidence-based curriculum reforms. My research seeks to integrate Cognitive Load Theory, emerging machine learning algorithms within artificial intelligence (AI), and statistical approaches to develop a quantitative framework for optimizing curriculum design within the PharmD program at the University of Toronto, the largest PharmD program within Canada, to provide quantification and measurement of issues that currently are only discussed in terms of anecdote rather than data. This research will serve as a guide for curriculum planners, administrators, and educators, aiding in the comprehension of how the pharmacy degree program compares to others within and beyond the field of pharmacy. It will also shed light on opportunities to reduce the curricular load while maintaining its quality and rigor. Given that pharmacists constitute the third-largest healthcare workforce, their education shares similarities and challenges with other health education programs. Therefore, my evidence-based, data-driven curriculum analysis framework holds significant potential for training programs in other healthcare professions, including medicine, nursing, and physiotherapy.

Keywords: curriculum, curriculum analysis, health professions education, reflective writing, machine learning

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Evgenia Papathanasiou, Georgios Kokkotis, Georgios Axiaris, Theodoros Argyropoulos, Nikos Viazis, Olga Giouleme, Konstantinos Gkoumas, Αnthia Gatopoulou, Αggelos Theodoulou, Georgios Theocharis, Αngeliki Theodoropoulou, Μaria Κalogirou, Pantelis Karatzas, Κonstantinos Κatsanos, Theodora Kafetzi, Κonstantinos Κarmiris, Αnastasia Κourikou, Ιoannis E Κoutroubakis, Christos Liatsos, Gerassimos J. Mantzaris, Νicoletta Μathou, Georgia Bellou, George Michalopoulos Αikaterini Μantaka, Penelope Nikolaou, Μichael Oikonomou, Dimitrios Polymeros, George Papatheodoridis, Εvdoxia Stergiou, Κonstantinos Soufleris, Εpameinondas Skouloudis, Μaria Tzouvala, Georgia Tsiolakidou, Εftychia Tsironi, Styliani Tsafaraki, Kalliopi Foteinogiannopoulou, Konstantina Chalakatevaki, Αngeliki Christidou, Dimitrios K. Christodoulou, Giorgos Bamias, Spyridon Michopoulos, Εvanthia Zampeli

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Background: Inflammatory bowel disease (IBD) commonly affects female patients of reproductive age, making the interaction between fertility, pregnancy and IBD an important issue in disease management. The effect of disease activity on the outcome of pregnancy and its impact on neonatal growth is a field of intense research. Close follow-up of pregnant IBD patients by a multidisciplinary team improves maternal and neonatal outcomes. Aim – Methods: Α national retrospective study of pregnancies in women with IBD between 2010-2020 was carried out in 22 IBD reference centers in Greece. Patient characteristics such as disease profile, type of treatment, and disease activity during gestation were analyzed in correlation to the method of delivery, pregnancy outcomes, as well as breastfeeding and offspring health. Results: Two-hundred and twenty-three pregnancies in 175 IBD patients were registered in the study. 122 with Crohn’s disease (CD). Median age during diagnosis was 25.6 years (12-44), with median disease duration of 7.4 years (0-23). One-hundred and twenty-nine patients (58%) were recorded during their first pregnancy. Early pregnancy termination was reported by 48 patients (22%). Pregnancy as a result of in vitro fertilization (IVF) occurred in 15 cases (6.7%). At the beginning of gestation, 165 patients (74%) were under treatment: 48 with anti-TNF agents (29%), 43 with azathioprine (26%), 101 with 5-aminosalicylic acid formulations (61%) and 12 with steroids (7%). We recorded 49 cases of IBD flares (22%) during pregnancy. Two-thirds of them (n=30) were in remission at the onset of the pregnancy. Almost half of them (n=22) required corticosteroid treatment. Patients with ulcerative colitis (UC) were in greater risk of disease flare during pregnancy (p<0.001). All but 3 pregnancies (99.1%) resulted in uncomplicated delivery. In 147 cases (67.1%), cesarean delivery was performed. Two late fetal deaths (0.9%) were reported, both in patients with continuously active disease since the beginning of pregnancy. After delivery, 75 patients (34%) presented with a disease flare, which was associated with active disease at the beginning of pregnancy (p <0.001). Conclusion: The majority of female, Greek IBD patients, had a favorable pregnancy outcome. Active inflammation during gestation and UC diagnosis were associated with a negative impact on pregnancy outcomes. The results of this study are in favor of the continuation of IBD treatment during pregnancy.

Keywords: pregnancy, ulcerative colitis, Crohn disease, flare

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Ramya P, Lino Abraham Varghese, S. Bose

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By cloud storage services, the data can be stored in the cloud, and can be shared across multiple users. Due to the unexpected hardware/software failures and human errors, which make the data stored in the cloud be lost or corrupted easily it affected the integrity of data in cloud. Some mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. But public auditing on the integrity of shared data with the existing mechanisms will unavoidably reveal confidential information such as identity of the person, to public verifiers. Here a privacy-preserving mechanism is proposed to support public auditing on shared data stored in the cloud. It uses group signatures to compute verification metadata needed to audit the correctness of shared data. The identity of the signer on each block in shared data is kept confidential from public verifiers, who are easily verifying shared data integrity without retrieving the entire file. But on demand, the signer of the each block is reveal to the owner alone. Group private key is generated once by the owner in the static group, where as in the dynamic group, the group private key is change when the users revoke from the group. When the users leave from the group the already signed blocks are resigned by cloud service provider instead of owner is efficiently handled by efficient proxy re-signature scheme.

Keywords: data integrity, dynamic group, group signature, public auditing

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Authors: Nathainail Bashir, Neil Anderson

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The objective of this study site was to investigate the current state of the practice with regards to karst detection methods and recommend the best method and pattern of arrays to acquire the desire results. Proper site investigation in karst prone regions is extremely valuable in determining the location of possible voids. Two geophysical techniques were employed: multichannel analysis of surface waves (MASW) and electric resistivity tomography (ERT).The MASW data was acquired at each test location using different array lengths and different array orientations (to increase the probability of getting interpretable data in karst terrain). The ERT data were acquired using a dipole-dipole array consisting of 168 electrodes. The MASW data was interpreted (re: estimated depth to physical top of rock) and used to constrain and verify the interpretation of the ERT data. The ERT data indicates poorer quality MASW data were acquired in areas where there was significant local variation in the depth to top of rock.

Keywords: dipole-dipole, ERT, Karst terrains, MASW

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Authors: H. W. Meerveld, R. H. A. Lindelauf

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In contemporary warfare, data science is crucial for the military in achieving information superiority. Yet, to the authors’ knowledge, no extensive literature survey on data science in military decision-making has been conducted so far. In this study, 156 peer-reviewed articles were analysed through an integrative, semi-systematic literature review to gain an overview of the topic. The study examined to what extent literature is focussed on the opportunities or risks of data science in military decision-making, differentiated per level of war (i.e. strategic, operational, and tactical level). A relatively large focus on the risks of data science was observed in social science literature, implying that political and military policymakers are disproportionally influenced by a pessimistic view on the application of data science in the military domain. The perceived risks of data science are, however, hardly addressed in formal science literature. This means that the concerns on the military application of data science are not addressed to the audience that can actually develop and enhance data science models and algorithms. Cross-disciplinary research on both the opportunities and risks of military data science can address the observed research gaps. Considering the levels of war, relatively low attention for the operational level compared to the other two levels was observed, suggesting a research gap with reference to military operational data science. Opportunities for military data science mostly arise at the tactical level. On the contrary, studies examining strategic issues mostly emphasise the risks of military data science. Consequently, domain-specific requirements for military strategic data science applications are hardly expressed. Lacking such applications may ultimately lead to a suboptimal strategic decision in today’s warfare.

Keywords: data science, decision-making, information superiority, literature review, military

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Authors: Amber Dhoot, Tarush Gupta, Andrea Gurr, William Jenkins, Sandro Pietrunti, Alexis Tang

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Background: The COVID-19 pandemic has driven pharmacovigilance towards a new paradigm. Nowadays, more people than ever before are recognising and reporting adverse reactions from medications, treatments, and vaccines. In the modern era, with over 3.8 billion users, social media has become the most accessible medium for people to voice their opinions and so provides an opportunity to engage with more patient-centric and accessible pharmacovigilance. However, the pharmaceutical industry has been slow to incorporate social media into its modern pharmacovigilance strategy. This project aims to make social media a more effective tool in pharmacovigilance, and so reduce drug costs, improve drug safety and improve patient outcomes. This will be achieved by firstly uncovering and categorising the barriers facing the widespread adoption of social media in pharmacovigilance. Following this, the potential opportunities of social media will be explored. We will then propose realistic, practical recommendations to make social media a more effective tool for pharmacovigilance. Methodology: A comprehensive systematic literature review was conducted to produce a categorised summary of these barriers. This was followed by conducting 11 semi-structured interviews with pharmacovigilance experts to confirm the literature review findings whilst also exploring the unpublished and real-life challenges faced by those in the pharmaceutical industry. Finally, a survey of the general public (n = 112) ascertained public knowledge, perception, and opinion regarding the use of their social media data for pharmacovigilance purposes. This project stands out by offering perspectives from the public and pharmaceutical industry that fill the research gaps identified in the literature review. Results: Our results gave rise to several key analysis points. Firstly, inadequacies of current Natural Language Processing algorithms hinder effective pharmacovigilance data extraction from social media, and where data extraction is possible, there are significant questions over its quality. Social media also contains a variety of biases towards common drugs, mild adverse drug reactions, and the younger generation. Additionally, outdated regulations for social media pharmacovigilance do not align with new, modern General Data Protection Regulations (GDPR), creating ethical ambiguity about data privacy and level of access. This leads to an underlying mindset of avoidance within the pharmaceutical industry, as firms are disincentivised by the legal, financial, and reputational risks associated with breaking ambiguous regulations. Conclusion: Our project uncovered several barriers that prevent effective pharmacovigilance on social media. As such, social media should be used to complement traditional sources of pharmacovigilance rather than as a sole source of pharmacovigilance data. However, this project adds further value by proposing five practical recommendations that improve the effectiveness of social media pharmacovigilance. These include: prioritising health-orientated social media; improving technical capabilities through investment and strategic partnerships; setting clear regulatory guidelines using multi-stakeholder processes; creating an adverse drug reaction reporting interface inbuilt into social media platforms; and, finally, developing educational campaigns to raise awareness of the use of social media in pharmacovigilance. Implementation of these recommendations would speed up the efficient, ethical, and systematic adoption of social media in pharmacovigilance.

Keywords: adverse drug reaction, drug safety, pharmacovigilance, social media

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Authors: Oren Shtayermman

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The current investigation expended on research among parents caring for a child who is diagnosed with an autism spectrum disorder (ASD). An online web survey was used to collect data from 253 parents caring for a child with a diagnosis of ASD. Both parents reported on elevated levels of parental stress associated with caring for the child on the spectrum. In addition, lower levels of marital satisfaction were found in both parents. About 13% of the parents in the sample met the diagnostic criteria for Major Depressive Disorder and About 15% of the parents met the diagnostic criteria for Generalized Anxiety Disorder. Although the majority of the sample was females (94%) significant differences were found between males and females in relation to meeting the diagnostic criteria for Major Depressive Disorder and for Generalized Anxiety Disorder. Higher levels of stress were associated with higher number of Generalized Anxiety Disorder symptoms and higher number of Major Depressive Disorder symptoms. Findings from this study indicate how vulnerable parents and especially females are in relation to caring to a child diagnosed with ASD. Educational Objectives: At the conclusion of the paper, the readers should be able to: -Identify levels of stress and marital satisfaction among parents caring for a child diagnosed with autism spectrum disorder, -Recognize the impact of stress on the development of mental health issues, -Name the two most common mood and anxiety related disorders associated with caring for a child diagnosed with an autism spectrum disorder.

Keywords: autism, stress, parents, children

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Authors: Cai Qianyi

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In the midst of global digital revolution, the flow of data poses security threats that call China's existing legislative framework for protecting personal information into question. As a preliminary procedure for risk analysis and prevention, the risk assessment of personal data transmission lacks detailed guidelines for support. Existing provisions reveal unclear responsibilities for network operators and weakened rights for data subjects. Furthermore, the regulatory system's weak operability and a lack of industry self-regulation heighten data transmission hazards. This paper aims to compare the regulatory pathways for data information transmission risks between China and Europe from a legal framework and content perspective. It draws on the “Data Protection Impact Assessment Guidelines” to empower multiple stakeholders, including data processors, controllers, and subjects, while also defining obligations. In conclusion, this paper intends to solve China's digital security shortcomings by developing a more mature regulatory framework and industry self-regulation mechanisms, resulting in a win-win situation for personal data protection and the development of the digital economy.

Keywords: personal information data transmission, risk assessment, DPIA, internet service provider, personal information data transimission, risk assessment

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Authors: Habiba Ben Abdessalem

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The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period.

Keywords: textual data, wavelet, denoising, contingency table

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Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

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Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

Procedia PDF Downloads 307

Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

Abstract:

Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: N. R. N. Idris, S. Baharom

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A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates. On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.

Keywords: aggregate data, combined-level data, individual patient data, meta-analysis

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Authors: M. Shemis, O. Maher, G. Casterou, F. Gauffre

Abstract:

Hepatitis C virus (HCV) is a major cause of chronic liver disease with a global 170 million chronic carriers at risk of developing liver cirrhosis and/or liver cancer. Egypt reports the highest prevalence of HCV worldwide. Currently, two classes of assays are used in the diagnosis and management of HCV infection. Despite the high sensitivity and specificity of the available diagnostic assays, they are time-consuming, labor-intensive, expensive, and require specialized equipment and highly qualified personal. It is therefore important for clinical and economic terms to develop a low-tech assay for the direct detection of HCV RNA with acceptable sensitivity and specificity, short turnaround time, and cost-effectiveness. Such an assay would be critical to control HCV in developing countries with limited resources and high infection rates, such as Egypt. The unique optical and physical properties of gold nanoparticles (AuNPs) have allowed the use of these nanoparticles in developing simple and rapid colorimetric assays for clinical diagnosis offering higher sensitivity and specificity than current detection techniques. The current research aims to develop a detection assay for HCV RNA using gold nanoparticles (AuNPs). Methods: 200 anti-HCV positive samples and 50 anti-HCV negative plasma samples were collected from Egyptian patients. HCV viral load was quantified using m2000rt (Abbott Molecular Inc., Des Plaines, IL). HCV genotypes were determined using multiplex nested RT- PCR. The assay is based on the aggregation of AuNPs in presence of the target RNA. Aggregation of AuNPs causes a color shift from red to blue. AuNPs were synthesized using citrate reduction method. Different sets of probes within the 5’ UTR conserved region of the HCV genome were designed, grafted on AuNPs and optimized for the efficient detection of HCV RNA. Results: The nano-gold assay could colorimetrically detect HCV RNA down to 125 IU/ml with sensitivity and specificity of 91.1% and 93.8% respectively. The turnaround time of the assay is < 30 min. Conclusions: The assay allows sensitive and rapid detection of HCV RNA and represents an inexpensive and simple point-of-care assay for resource-limited settings.

Keywords: HCV, gold nanoparticles, point of care, viral load

Procedia PDF Downloads 203

Authors: Hyun-Woo Cho

Abstract:

The monitoring of industrial production quality has to be implemented to alarm early warning for unusual operating conditions. Furthermore, identification of their assignable causes is necessary for a quality control purpose. For such tasks many multivariate statistical techniques have been applied and shown to be quite effective tools. This work presents a process data-based monitoring scheme for production processes. For more reliable results some additional steps of noise filtering and preprocessing are considered. It may lead to enhanced performance by eliminating unwanted variation of the data. The performance evaluation is executed using data sets from test processes. The proposed method is shown to provide reliable quality control results, and thus is more effective in quality monitoring in the example. For practical implementation of the method, an on-line data system must be available to gather historical and on-line data. Recently large amounts of data are collected on-line in most processes and implementation of the current scheme is feasible and does not give additional burdens to users.

Keywords: detection, filtering, monitoring, process data

Procedia PDF Downloads 549

Authors: Vish Putcha, Chandrasekhar Putcha, Siva Hari

Abstract:

In the current digital era of free trading and pandemic-driven remote work culture, markets worldwide gained momentum for retail investors to trade from anywhere easily. The number of retail traders rose to 24% of the market from 15% at the pre-pandemic level. Most of them are young retail traders with high-risk tolerance compared to the previous generation of retail traders. This trend boosted the growth of subscription-based market predictors and market data vendors. Young traders are betting on these predictors, assuming one of them is correct. However, 90% of retail traders are on the losing end. This paper presents multiple indicators and attempts to derive behavioral patterns from the underlying stocks. The two major indicators that traders and investors follow are technical and fundamental. The famous investor, Warren Buffett, adheres to the “Value Investing” method that is based on a stock’s fundamental Analysis. In this paper, we present multiple indicators from various methods to understand the behavior patterns of stocks. For this research, we picked five stocks with a market capitalization of more than $200M, listed on the exchange for more than 20 years, and from different industry sectors. To study the behavioral pattern over time for these five stocks, a total of 8 indicators are chosen from fundamental, technical, and financial indicators, such as Price to Earning (P/E), Price to Book Value (P/B), Debt to Equity (D/E), Beta, Volatility, Relative Strength Index (RSI), Moving Averages and Dividend yields, followed by detailed mathematical Analysis. This is an interdisciplinary paper between various disciplines of Engineering, Accounting, and Finance. The research takes a new approach to identify clear indicators affecting stocks. Statistical Analysis of the data will be performed in terms of the probabilistic distribution, then follow and then determine the probability of the stock price going over a specific target value. The Chi-square test will be used to determine the validity of the assumed distribution. Preliminary results indicate that this approach is working well. When the complete results are presented in the final paper, they will be beneficial to the community.

Keywords: stock pattern, stock market analysis, stock predictions, trading, investing, fundamental analysis, technical analysis, quantitative trading, financial analysis, behavioral analysis

Procedia PDF Downloads 76