Search results for: data exploitation

Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: Nazura Abdul Manap, Siti Nur Farah Atiqah Salleh

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For decades, the misuse of personal data has been a critical issue. Malaysia has accepted responsibility by implementing the Malaysian Personal Data Protection Act 2010 to secure personal data (PDPA 2010). After more than a decade, this legislation is set to be revised by the current PDPA 2023 Amendment Bill to align with the world's key personal data protection regulations, such as the European Union General Data Protection Regulations (GDPR). Among the other suggested adjustments is the Data User's appointment of a Data Protection Officer (DPO) to ensure the commercial entity's compliance with the PDPA 2010 criteria. The change is expected to be enacted in parliament fairly soon; nevertheless, based on the experience of the Personal Data Protection Department (PDPD) in implementing the Act, it is projected that there will be a slew of additional concerns associated with the DPO mandate. Consequently, the goal of this article is to highlight the issues that the DPO will encounter and how the Personal Data Protection Department should respond to this subject. The study result was produced using a qualitative technique based on an examination of the current literature. This research reveals that there are probable obstacles experienced by the DPO, and thus, there should be a definite, clear guideline in place to aid DPO in executing their tasks. It is argued that appointing a DPO is a wise measure in ensuring that the legal data security requirements are met.

Keywords: guideline, law, data protection officer, personal data

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Authors: Moayyad Shawaqfah, Ibtehal Alqdah, Amjad Adaileh

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Coridoor area (400 km2) lies to the north – east of Amman (60 km). It lies between 285-305 E longitude and 165-185 N latitude (according to Palestine Grid). It been subjected to exploitation of groundwater from new eleven wells since the 1999 with a total discharge of 11 MCM in addition to the previous discharge rate from the well field 14.7 MCM. Consequently, the aquifer balance is disturbed and a major decline in water level. Therefore, suitable groundwater resources management is required to overcome the problems of over pumping and its effect on groundwater quality. Three–dimensional groundwater flow model Processing Modeflow for Windows Pro (PMWIN PRO, 2003) has been used in order to calculate the groundwater budget, aquifer characteristics, and to predict the aquifer response under different stresses for the next 20 years (2035). The model was calibrated for steady state conditions by trial and error calibration. The calibration was performed by matching observed and calculated initial heads for year 2001. Drawdown data for period 2001-2010 were used to calibrate transient model by matching calculated with observed one, after that, the transient model was validated by using the drawdown data for the period 2011-2014. The hydraulic conductivities of the Basalt- A7/B2 aquifer System are ranging between 1.0 and 8.0 m/day. The low conductivity value was found at the north-west and south-western parts of the study area, the high conductivity value was found at north-western corner of the study area and the average storage coefficient is about 0.025. The water balance for the Basalt and B2/A7 formation at steady state condition with a discrepancy of 0.003%. The major inflows come from Jebal Al Arab through the basalt and through the limestone aquifer (B2/A7 12.28 MCMY aquifer and from excess rainfall is about 0.68 MCM/a. While the major outflows from the Basalt-B2/A7 aquifer system are toward Azraq basin with about 5.03 MCMY and leakage to A1/6 aquitard with 7.89 MCMY. Four scenarios have been performed to predict aquifer system responses under different conditions. Scenario no.2 was found to be the best one which indicates that the reduction the abstraction rates by 50% of current withdrawal rate (25.08 MCMY) to 12.54 MCMY. The maximum drawdowns were decreased to reach about, 7.67 and 8.38m in the years 2025 and 2035 respectively.

Keywords: Amman/Zarqa Basin, Jordan, groundwater management, groundwater modeling, modflow

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Authors: Nouha Mhimdi, Wahiba Ben Abdessalem Karaa, Henda Ben Ghezala

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The methods identified in data collection are diverse: electronic media, focus group interviews and short-answer questionnaires [1]. The collection of poor-quality data resulting, for example, from poorly designed questionnaires, the absence of good translators or interpreters, and the incorrect recording of data allow conclusions to be drawn that are not supported by the data or to focus only on the average effect of the program or policy. There are several solutions to avoid or minimize the most frequent errors, including obtaining expert advice on the design or adaptation of data collection instruments; or use technologies allowing better "anonymity" in the responses [2]. In this context, we opted to collect good quality data by doing a sizeable questionnaire-based survey on hospital emergencies to improve emergency services and alleviate the problems encountered. At the level of this paper, we will present our study, and we will detail the steps followed to achieve the collection of relevant, consistent and practical data.

Keywords: data collection, survey, questionnaire, database, data analysis, hospital emergencies

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Authors: Daniela Pellin, Wilson Engelmann

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Brazil has sought to accelerate your development through technology and innovation as a response to the global influences, which has received in internal management practices. For this, it had edited the Brazilian Law of Innovation 13.243/2016. However observing the Law overestimated economic aspects the respective application will not consider the stakeholders and the technological risks because there is no legal treatment. The economic exploitation and the technological risks must be controlled by limits of democratic system to find better social development to contribute with the economics agents for making decision to conform with global directions. The research understands this is a problem to face given the social particularities of the country because there has been the literal import of the North American Triple Helix Theory consolidated in developed countries and the negative consequences when applied in developing countries. Because of this symptomatic scenario, it is necessary to create adjustment to conduct the management of the law besides social democratic interests to increase the country development. For this, therefore, the Government will have to adopt some conducts promoting side by side with universities, civil society and companies, informational transparency, catch of partnerships, create a Confort Letter document for preparation to ensure the operation, joint elaboration of a Manual of Good Practices, make accountability and data dissemination. Also the Universities must promote informational transparency, drawing up partnership contracts and generating revenue, development of information. In addition, the civil society must do data analysis about proposals received for discussing to give opinion related. At the end, companies have to give public and transparent information about investments and economic benefits, risks and innovation manufactured. The research intends as a general objective to demonstrate that the efficiency of the propeller deployment will be possible if the innovative decision-making process goes through the institutional logic. As specific objectives, the American influence must undergo some modifications to better suit the economic-legal incentives to potentiate the development of the social system. The hypothesis points to institutional model for application to the legal system can be elaborated based on emerging characteristics of the country, in such a way that technological risks can be foreseen and there will be global conformity with attention to the full development of society as proposed by the researchers.The method of approach will be the systemic-constructivist with bibliographical review, data collection and analysis with the construction of the institutional and democratic model for the management of the Law.

Keywords: development, governance of law, institutionalization, triple helix

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

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Authors: Fahad Alamoudi, Yaser Miaji

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The growing popularity of swimming pools and other activities in the water for sport, fitness, therapy or just enjoyable relaxation have led to the increased use of swimming pools and the establishment of a variety of specific-use pools such as spa pools, water slides, and more recently, hydrotherapy and wave pools. In this research, a few simple equipment is used for test, detect and alert for detection of water cleanness and pollution. YSI Photometer Systems, TDSTestr High model, Rio 12HF and Electrode A1. The researchers used electrolysis as a method of separating bonded elements and compounds by passing an electric current through them. The results which use 41 experiments show the higher the salt concentration, the more efficient the electrode and the smaller the gap between the plates, the lower the electrode voltage. Furthermore, it is proved that the larger the surface area, the lower the cell voltage and the higher current used the more chlorine produced.

Keywords: photometer, electrode, electrolysis, swimming pool chlorination

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Authors: Daniel Brian Thompson, Subarmaniam Kannan

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The huge weightage of knowledge in this world and within the repository of organizations has already reached immense capacity and is constantly increasing as time goes by. To accommodate these constraints, Big Data implementation and algorithms are utilized to obtain new or enhanced knowledge for decision-making. With the transition from data to knowledge provides the transformational changes which will provide tangible benefits to the individual implementing these practices. Today, various organization would derive knowledge from observations and intuitions where this information or data will be translated into best practices for knowledge acquisition, generation and sharing. Through the widespread usage of Big Data, the main intention is to provide information that has been cleaned and analyzed to nurture tangible insights for an organization to apply to their knowledge-creation practices based on facts and figures. The translation of data into knowledge will generate value for an organization to make decisive decisions to proceed with the transition of best practices. Without a strong foundation of knowledge and Big Data, businesses are not able to grow and be enhanced within the competitive environment.

Keywords: big data, knowledge management, data driven, knowledge creation

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Authors: Masese Chuma Benard, Martin Onsiro Ronald

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Businesses have been using cloud computing more frequently recently because they wish to take advantage of its advantages. However, employing cloud computing also introduces new security concerns, particularly with regard to data security, potential risks and weaknesses that could be exploited by attackers, and various tactics and strategies that could be used to lessen these risks. This study examines data security issues on cloud computing amongst sme’s in Nairobi county, Kenya. The study used the sample size of 48, the research approach was mixed methods, The findings show that data owner has no control over the cloud merchant's data management procedures, there is no way to ensure that data is handled legally. This implies that you will lose control over the data stored in the cloud. Data and information stored in the cloud may face a range of availability issues due to internet outages; this can represent a significant risk to data kept in shared clouds. Integrity, availability, and secrecy are all mentioned.

Keywords: data security, cloud computing, information, information security, small and medium-sized firms (SMEs)

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Authors: M. Strémy, P. Závacký, P. Cuninka, M. Juhás

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Main goal of this paper is to introduce our design of private cloud for storing large amount of data, especially pictures, and to provide good technological backend for data analysis based on parallel processing and business intelligence. We have tested hypervisors, cloud management tools, storage for storing all data and Hadoop to provide data analysis on unstructured data. Providing high availability, virtual network management, logical separation of projects and also rapid deployment of physical servers to our environment was also needed.

Keywords: cloud, glusterfs, hadoop, juju, kvm, maas, openstack, virtualization

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Authors: Agoun-bahar Salima

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The environmental variations to which plants are subjected require them to have a strong capacity for adaptation. Some plants are affected by pollutants and are used as pollution indicators; others have the capacity to block, extract, accumulate, transform or degrade the xenobiotic. The diversity of the legume family includes around 20 000 species and offers opportunities for exploitation through their agronomic, dietary and ecological interests. The lack of data on the bioavailability of the Aromatic Polycyclic Hydrocarbon (PAH) in polluted environments, as their passage in the food chains and on the effects of interaction with other pollutants, justifies priority research on this vast family of hydrocarbons. Naphthalene is a PAH formed from two aromatic rings, it is listed and classified as priority pollutant in the list of 16 PAH by the United States Environmental Protection Agency. The aim of this work was to determinate effect of naphthalene at different concentrations on morphological and physiological responses of pea seedlings. At the same time, the behavior of the pollutant in the soil and its fate at the different parts of plant (roots, stems, leaves and fruits) were also recorded by Gas Chromatography/ Mass Spectrometry (GC / MS). In it controlled laboratory studies, plants exposed to naphthalene were able to grow efficiently. From a quantitative analysis, 67% of the naphthalene was removed from the soil and then found on the leaves of the seedlings in just three weeks of cultivation. Interestingly, no trace of naphthalene or its derivatives were detected on the chromatograms corresponding to the dosage of the pollutant at the fruit level after ten weeks of cultivating the seedlings and this for all the pollutant concentrations used. The pea seedlings seem to tolerate the pollutant when it is applied to the soil. In conclusion, the pea represents an interesting biological model in the study of phytoremediation mechanisms.

Keywords: naphtalene, PAH, Pea, phytoremediation, pollution

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Authors: Firdaus Khairi Abdul Kadir, Nazihah Rusli, Noor Aisyah Abdul Aziz

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Islam is a comprehensive religion encompassing all aspects of society’s life such as social, economic, political, cultural and environmental. The environment is part of the manifestation of God’s greatness which has pearls of wisdom, bestowed upon human beings to make them realize that everything is in the hands of God (Allah SWT). However, the equilibrium of nature could be disturbed from the excessive exploitation by humans’ hands. As a caliph on this earth, it is the responsibility of human beings to look after the environment proactively. Besides, Islam calls for the execution of accountable development and respecting the principles of sustainability. Therefore, this study focuses on the role of human beings as caliphs on this earth who are responsible for nature and their acts in conserving and preserving the environment based on the approach of religious education. This study also used the research method of the survey library.

Keywords: environment, human beings, caliph, tauhid, Allah SWT

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Marinos Xagoraris, Iliada K. Lappa, Charalambos Kanakis, Dimitra Daferera, Christina Papadopoulou, Georgios Sourounis, Charilaos Giotis, Pavlos Bouchagier, Christos S. Pappas, Petros A. Tarantilis, Efstathia Skotti

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The aim of this work was to recover phenolic compounds from grape skins produced in Greek varieties of the Ionian Islands in order to form the basis of calculations for their further utilization in the context of the circular economy. Isolation and further utilization of phenolic compounds is an important issue in winery by-products. For this purpose, 37 samples were collected, extracted, and analyzed in an attempt to provide the appropriate basis for their sustainable exploitation. Extraction of the bioactive compounds was held using an eco-friendly, non-toxic, and highly effective water-glycerol solvent system. Then, extracts were analyzed using UV-Vis, liquid chromatography-mass spectrometry (LC-MS), FTIR, and Raman spectroscopy. Also, total phenolic content and antioxidant activity were measured. LC-MS chromatography showed qualitative differences between different varieties. Peaks were attributed to monomeric 3-flavanols as well as monomeric, dimeric, and trimeric proanthocyanidins. The FT-IR and Raman spectra agreed with the chromatographic data and contributed to identifying phenolic compounds. Grape skins exhibited high total phenolic content (TPC), and it was proved that during vinification, a large number of polyphenols remained in the pomace. This study confirmed that grape skins from Ionian Islands are a promising source of bioactive compounds, suggesting their utilization under a bio-economic and environmental strategic framework.

Keywords: antioxidant activity, grape skin, phenolic compounds, waste recovery

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Authors: Melaku Tsehay

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The Consortium of Christian Relief and Development Associations (CCRDA), and the CORE Group Polio Partners (CGPP) Secretariat have been working with Global Alliance for Vac-cines and Immunization (GAVI) to improve the immunization data quality in Afar and Somali Regional States. The main aim of this study was to compare the quality of immunization data before and after the above interventions in health facilities in the pastoralist communities in Ethiopia. To this end, a comparative-cross-sectional study was conducted on 51 health facilities. The baseline data was collected in May 2019, while the end line data in August 2021. The WHO data quality self-assessment tool (DQS) was used to collect data. A significant improvment was seen in the accuracy of the pentavalent vaccine (PT)1 (p = 0.012) data at the health posts (HP), while PT3 (p = 0.010), and Measles (p = 0.020) at the health centers (HC). Besides, a highly sig-nificant improvment was observed in the accuracy of tetanus toxoid (TT)2 data at HP (p < 0.001). The level of over- or under-reporting was found to be < 8%, at the HP, and < 10% at the HC for PT3. The data completeness was also increased from 72.09% to 88.89% at the HC. Nearly 74% of the health facilities timely reported their respective immunization data, which is much better than the baseline (7.1%) (p < 0.001). These findings may provide some hints for the policies and pro-grams targetting on improving immunization data qaulity in the pastoralist communities.

Keywords: data quality, immunization, verification factor, pastoralist region

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

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Authors: Zi Wang, Xinlei He, Jianghua Lv, Yuqing Lan

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Kernel though widely used, is complicated. Errors caused by some bugs are often costly. Statically, more than half of the mistakes occur in the design phase. Thus, we introduce a modeling method, KMVM (Linux Kernel Modeling and verification Method), based on type theory for proper designation and correct exploitation of the Kernel. In the model, the Kernel is separated into six levels: subsystem, dentry, file, struct, func, and base. Each level is treated as a type. The types are specified in the structure and relationship. At the same time, we use a demanding path to express the function to be implemented. The correctness of the design is verified by recursively checking the type relationship and type existence. The method has been applied to verify the OPEN business of VFS (virtual file system) in Linux Kernel. Also, we have designed and developed a set of security communication mechanisms in the Kernel with verification.

Keywords: formal approach, type theory, Linux Kernel, software program

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Utkarsh Goley

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BDSM, or Bondage Discipline Sadism Masochism, is a controversial and often misunderstood practice that has had a presence in the fashion industry for decades. BDSM-inspired fashion can be seen in various forms, from leather harnesses and corsets to studded collars and latex clothing. BDSM fashion is often associated with edginess, rebellion, and sexuality. It has been embraced by subcultures such as punk, Goth, and fetish, as well as mainstream fashion designers looking to push boundaries and make a statement. However, the use of BDSM imagery in fashion has also been criticized for promoting objectification, exploitation, and the normalization of abusive behavior. Some argue that the fashion industry's depiction of BDSM often reinforces harmful stereotypes and misconceptions about the practice. Despite the controversy, BDSM-inspired fashion continues to have a place in the industry, with designers and consumers alike finding value in its aesthetic appeal and provocative nature. As with any aspect of fashion, the role of BDSM in the industry will continue to evolve and adapt to changing cultural norms and societal attitudes.

Keywords: BDSM, leather, fashion, lycra

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Authors: Simiso Fisokuhle Nyandeni

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The environment is one of the major components of intergenerational equity under sustainability; however, this component has been facing a lot of issues/crises, which include those that are caused by natural systems due to the actions of humans. Although some of those environmental issues may occur from natural causes, however, climate change effects have shown to increase rapidly due to human behavior, which led to the increase in greenhouse emissions and the over-exploitation of natural resources that maintain an ecological balance in our environment. Based on the recent projections, the growing population tends to outstrip the environmental resources, and as a result, the rapid depletion of natural resources that maintain ecological balance within the environment has resulted in such environmental issues. This paper has adopted desktop analysis to address the main objective, which seeks to address the effects of population dynamics on environmental issues and what needs to be done to maintain the ecological balance between the growing population and the limited resources that are available; thus, the collective data sources were used to justify the literature in order to get adequate results to influence the potential findings. The major findings postulate that there is an ecological imbalance between limited resources available and the growing population; as a result, the environment is taking action against humanity through climate change impacts. Hence findings further outline that in order to prevent such impacts, there should be drastic interventions by the governments (all stakeholders should be involved in decision-making; Governmental or non-governmental institutions, scientists, researchers, etc.) around the world to maintain this ecological balance and also to prioritize the adaptation measures. Therefore, this paper seeks to examine the implications of population dynamics on the environmental issues and what needs to be done in order to maintain this ecological balance between the growing population and environmental resources; hence, this review will be based on the climate change context.

Keywords: population dynamics, climate change, environment, sustainability

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Authors: Christopher Muhawe, Masooda Bashir

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With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: S. S. Rwanga, J. M. Ndambuki, Y. Woyessa

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Development of a groundwater management model is an important step in the exploitation and management of any groundwater aquifer as it assists in the long-term sustainable planning of the resource. The current study was conducted in Central Limpopo province of South Africa with the overall objective of determining how much water can be withdrawn from the aquifer without producing nonreversible impacts on the groundwater quantity, hence developing a model which can sustainably protect the aquifer. The development was done through the computation of Groundwater Sustainability Index (GSI). Values of GSI close to unity and above indicated overexploitation. In this study, an index of 0.8 was considered as overexploitation. The results indicated that there is potential for higher abstraction rates compared to the current abstraction rates. GSI approach can be used in the management of groundwater aquifer to sustainably develop the resource and also provides water managers and policy makers with fundamental information on where future water developments can be carried out.

Keywords: development, groundwater, groundwater sustainability index, model

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Authors: Chuping Lo

Abstract:

This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

Abstract:

Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Fuad M. Alkoot

Abstract:

DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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