Search results for: Privacy Preserving Data Publication (PPDP)

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: Victoria Legkikh

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To create a new Orthodox service of Jerusalem rite and to make it possible for a performance, one had to use several types of books. These are menaions and triodion, cleargy service book, stichirarion and typikon. These books keep a part of the information about the service, which a medieval copyist had to put together like a puzzle. But an abundance of necessary books and their variety created a lot of problems in copying services. The main problem was the difference of text in notated and not notated manuscripts (they were corrected at a different time) and lack of information in typikon, which provided only a type of hymns and their mode. After all, a copyist could have both corrected and not corrected manuscripts which also provided a different type of service. It brings us to the situation when we hardly have a couple of manuscripts containing the same service, and it is difficult to understand which changes were made voluntarily and which ones were provided by different types of available manuscripts. A recent paper proposes an analysis of every type of liturgical book and a way of using them in copying and correcting a service so we can divide voluntary changes and changes due to various types of books. The paper also proposes an index showing the “material” life of hymns in different types of manuscripts and the changes of its version and place in the same type of manuscript. This type of index can help in reconstructing the way of creation/copying service and can be useful for publication of the services providing necessary information of every hymn in every used manuscript.

Keywords: orthodox church music, creation, manuscripts, liturgical books

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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: Monika Florczak-Wątor

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The main purpose of this paper is to determine the applicability of the constitutional social rights in the so-called horizontal relations, i.e. the relations between private entities. Nowadays the constitutional rights are more and more often violated by private entities and not only by the state. The private entities interfere with the privacy of individuals, limit their freedom of expression or disturb their peaceful gatherings. International corporations subordinate individuals in a way which may limit their constitutional rights. These new realities determine the new role of the constitution in protecting human rights. The paper will aim at answering two important questions. Firstly, are the private entities obliged to respect the constitutional social rights of other private entities and can they be liable for violation of these rights? Secondly, how the constitutional social rights can receive horizontal effect? Answers to these questions will have a significant meaning for the popularization of the practice of applying the Constitution among the citizens as well as for the courts which settle disputes between them.

Keywords: social rights, private relations, horizontality, constitutional rights

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Bhavini Pandya

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This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

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Authors: Alsayed Ibrahim Diwedar, Ahmed ElKut, Mohamed Yossef

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Coastal problems are stressing the coastal environment due to its complexity. The dynamic interaction between the sea and the land results in serious problems that threaten coastal areas worldwide, in addition to human interventions and activities. This makes the coastal environment highly vulnerable to natural processes like flooding, erosion, and the impact of human activities as pollution. Protecting and preserving this vulnerable coastal zone with its valuable ecosystems calls for addressing the coastal problems. This, in the end, will support the sustainability of the coastal communities and maintain the current and future generations. Consequently applying suitable management strategies and sustainable development that consider the unique characteristics of the coastal system is a must. The coastal management philosophy aims to solve the conflicts of interest between human development activities and this dynamic nature. Modeling emerges as a successful tool that provides support to decision-makers, engineers, and researchers for better management practices. Modeling tools proved that they are accurate and reliable in prediction. With its capability to integrate data from various sources such as bathymetric surveys, satellite images, and meteorological data, it offers the possibility for engineers and scientists to understand this complex dynamic system and get in-depth into the interaction between both the natural and human-induced factors. Enabling decision makers to make informed choices and develop effective strategies for sustainable development and risk mitigation. The application of modeling tools supports the evaluation of various scenarios by affording the possibility to simulate and forecast different coastal processes from the hydrodynamic and wave actions and the resulting flooding and erosion. The state-of-the-art application of modeling tools in coastal management allows for better understanding and predicting coastal processes, optimizing infrastructure planning and design, supporting ecosystem-based approaches, assessing climate change impacts, managing hazards, and finally facilitating stakeholder engagement. This paper emphasizes the role of hydraulic models in enhancing the management of coastal problems by discussing the diverse applications of modeling in coastal management. It highlights the modelling role in understanding complex coastal processes, and predicting outcomes. The importance of informing decision-makers with modeling results which gives technical and scientific support to achieve sustainable coastal development and protection.

Keywords: coastal problems, coastal management, hydraulic model, numerical model, physical model

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Authors: Khalid Al-Snaie

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In this paper, the design and the testing of a symmetrical radiofrequency prototype coil with high B₁ magnetic field homogeneity are presented. The developed coil comprises four tuned coaxial circular loops that can produce a relatively homogeneous radiofrequency field. In comparison with a standard Helmholtz pair that provides 2nd-order homogeneity, it aims to provide fourth-order homogeneity of the B₁ field while preserving the simplicity of implementation. Electrical modeling of the probe, including all couplings, is used to ensure these requirements. Results of comparison tests, in free space and in a spectro-imager, between a standard Helmholtz pair and the presented prototype coil are introduced. In terms of field homogeneity, an improvement of 30% is observed. Moreover, the proposed prototype coil possesses a better quality factor (+25% on average) and a noticeable improvement in sensitivity (+20%). Overall, this work, which includes both theoretical and experimental aspects, aims to contribute to the study and understanding of four-element radio frequency (RF) systems derived from Helmholtz coils for Magnetic Resonance Imaging

Keywords: B₁ homogeneity, MRI, NMR, radiofrequency, RF coil, free element systems

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Authors: Hrishikesh Purandare, Ashwini Pethe

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Research from developed countries has demonstrated that the built environment can have a significant effect on children’s cognitive and socio-emotional development. A majority of the studies on the relationship between the built environment and the well-being of children have been conducted in North America and Western Europe, though most of the world’s children live in the global South. Millions of children living in urban slums in India confront issues associated with poor living conditions and lack of access to basic services. It is a well-known fact that slums are places of extreme poverty, substandard housing, overcrowding, and poor sanitation. These challenges faced by children living in slums can have a significant impact on their physical, psychological, and social development. Despite the magnitude of the problem, the area of research, particularly on the impact of the built environment of slums on children and adolescent well-being, has been understudied in India. Only a few studies in the global South have investigated the impact of the built environment on children’s well-being. Apart from issues of the limited access to health and education of these children, the perception of children regarding the built environment which they inhabit is rarely addressed. A sample of 120 children living in the slums of Pune city between the ages 7 and 16 participated in this study, which employed a concurrent embedded approach of mixed method research. Questionnaires were administered to obtain quantitative data that included attributes of crowding, noise, privacy, territoriality and housing quality in the built environment. The qualitative analysis of children’s sketches highlighted aspects of the built environment with which they associated themselves the most. The study sought to examine the perception of the deprived children living in the urban slums in the city of Pune (India) towards their built environment.

Keywords: physical environment, poverty, underprivileged children, urban Indian slums

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Authors: Zita Fomukong Andam

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The decision on where to host sport events does not depend on the national politicians or specific international sport event bodies but also involves the residents of the hosting country. Sport tourism is one of the fast growing industries in the world. Cameroonians consider sport as a point of unity and growth within the country. It has a huge variety of sporting activities like swimming, canoe racing, tug of war and most especially soccer well known as football. The football national team made an impact in 1990 at the FIFA world cup. They also won the African Nations Cup five times. Being the winner of the 2017 African Cup of Nations, they are to host the 2019 African cup of Nations. The purpose of this research is to analyse the impacts of sport tourism event in Cameroon and specifically examine how this event influences the residents. A deep research discourse conducted with randomly selected 300 inbound residents and 200 Cameroonian residents living abroad. Survey questionnaires, interviews and direct observations were carried out as a method of collecting data. The results showed that sport events brings a lot of prestige and honor to the country; generate revenues to the country’s economy and particularly to the local businesses. On the other hand, the results showed that the local residents lose their intimacy, privacy, and their daily life routine is affected. In addition to this, they face negative social inequalities and environmental impacts. Understanding these results the national government and international bodies might be able to contribute to future studies and propose efficient measures to maximize the positive benefits and minimize the negative benefits.

Keywords: sport Tourism, economic impact, resident altitude, african Cup of nations

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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: L. Ramirez, E. Guillén, J. Sánchez

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Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.

Keywords: analytics, telemedicine, internet of things, cloud computing

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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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: Hazem Abuorf

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From the perspective of viewing urban design as the architecture of public spaces, the latter has many advantages; for example, in achieving attractive and vibrant public spaces, ensuring safety and amenity, supporting a strong sense of place and local character. Besides all advantages, such spaces nevertheless trigger numerous dilemmas, how to design urban spaces preserving the quality of life in the long term while equally achieving cohesion between new urban developments and the already existing urban structure without causing a split in history through the cause of functional and aesthetic degradation. Analysis of this article seeks to propose a methodology deemed essential for assessing and stimulating design criteria of the public spaces when rehabilitating urban sites. The method’s utility is shown by analyzing rehabilitation projects in Gaza Strip, whose design of the public spaces has mainly focused on the physical aspect ignoring the place’s local identity, users’ needs, and history. The proposed methodology serves as a guide for municipal authorities and technical teams to deal with interventions that would rework the role of urban design towards making sense of place.

Keywords: urban design, public realm, rehabilitation projects, quality of life

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Authors: William Huang

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Retinal prostheses are currently limited to low resolution grayscale images that lack color and spatial information. This study develops a novel real-time image optimization framework and tools to encode maximum information to the prostheses which are constrained by the number of electrodes. One key idea is to localize main objects in images while reducing unnecessary background noise through region-contrast saliency maps. A novel color depth mapping technique was developed through MiniBatchKmeans clustering and color space selection. The resulting image was downsampled using bicubic interpolation to reduce image size while preserving color quality. In comparison to current schemes, the proposed framework demonstrated better visual quality in tested images. The use of the region-contrast saliency map showed improvements in efficacy up to 30%. Finally, the computational speed of this algorithm is less than 380 ms on tested cases, making real-time retinal prostheses feasible.

Keywords: retinal implants, virtual processing unit, computer vision, saliency maps, color quantization

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Authors: José Manuel Palma, Paula Pereira, Margarida Tomás

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Foreseen the future: Human factors integration in European Horizon Projects The development of new technology as artificial intelligence, smart sensing, robotics, cobotics or intelligent machinery must integrate human factors to address the need to optimize systems and processes, thereby contributing to the creation of a safe and accident-free work environment. Human Factors Integration (HFI) consistently pose a challenge for organizations when applied to daily operations. AGILEHAND and FORTIS projects are grounded in the development of cutting-edge technology - industry 4.0 and 5.0. AGILEHAND aims to create advanced technologies for autonomously sort, handle, and package soft and deformable products, whereas FORTIS focuses on developing a comprehensive Human-Robot Interaction (HRI) solution. Both projects employ different approaches to explore HFI. AGILEHAND is mainly empirical, involving a comparison between the current and future work conditions reality, coupled with an understanding of best practices and the enhancement of safety aspects, primarily through management. FORTIS applies HFI throughout the project, developing a human-centric approach that includes understanding human behavior, perceiving activities, and facilitating contextual human-robot information exchange. it intervention is holistic, merging technology with the physical and social contexts, based on a total safety culture model. In AGILEHAND we will identify safety emergent risks, challenges, their causes and how to overcome them by resorting to interviews, questionnaires, literature review and case studies. Findings and results will be presented in “Strategies for Workers’ Skills Development, Health and Safety, Communication and Engagement” Handbook. The FORTIS project will implement continuous monitoring and guidance of activities, with a critical focus on early detection and elimination (or mitigation) of risks associated with the new technology, as well as guidance to adhere correctly with European Union safety and privacy regulations, ensuring HFI, thereby contributing to an optimized safe work environment. To achieve this, we will embed safety by design, and apply questionnaires, perform site visits, provide risk assessments, and closely track progress while suggesting and recommending best practices. The outcomes of these measures will be compiled in the project deliverable titled “Human Safety and Privacy Measures”. These projects received funding from European Union’s Horizon 2020/Horizon Europe research and innovation program under grant agreement No101092043 (AGILEHAND) and No 101135707 (FORTIS).

Keywords: human factors integration, automation, digitalization, human robot interaction, industry 4.0 and 5.0

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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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: 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: Chuping Lo

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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

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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

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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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Authors: K. C. Surekha

Abstract:

The natural habitat forms the setting for every urban development. The tangible and intangible characteristics of the site contributed to the sustenance of various urban dimensions of early civilizations. However, as the towns were continuously evolving and developing, the attitude towards the natural habitat changed. The after effects of this self-centered attitude resulted in various natural and manmade catastrophes. At the same time the social habitat, cities and new towns were increasingly over-populated; and will become even more numerous and crowded in the future. The coexistence of natural and urban components is necessary for a sustainable future and preserving the region’s unique features. Therefore, there is an urgent need to rethink actively on alternative development paradigms to achieve sustenance of all living forms on the planet in a more sustainable way. The main aim of this paper is to understand bioregionalism as an alternative development paradigm, its theory, concepts as well as the key aspects of bioregional planning. The paper will try to understand the concept of bioregionalism theoretically and take case studies. The critical interpretation of theory and analysis of case studies will be used to form a set of design parameters which can be physically implemented from an urban design and planning standpoint.

Keywords: bioregion, bioregionalism, holistic, sustainable

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Authors: R Palson Kennedy, P.Kiran Sai

Abstract:

This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

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Authors: Okafor Calistus Chibuzor

Abstract:

Preserving African architectural heritage is a multifaceted endeavor that intersects with socio-cultural, economic, and environmental factors. Despite growing recognition of the importance of safeguarding these invaluable cultural assets, numerous challenges persist, hindering effective preservation efforts across the continent. This paper investigates the underlying factors impeding the preservation of African architectural heritage, aiming to provide insights for addressing this critical issue. The study begins with an exploration of the historical background and significance of African architectural heritage, highlighting its rich diversity and cultural significance. The study acknowledges that there is an urgent need to address the threats facing these heritage sites, including urbanization, rapid development, lack of funding, inadequate legal protection, and insufficient public awareness. The primary aim of this research is to identify and analyze the key factors contributing to the deterioration and loss of African architectural heritage, with the objective of formulating strategies to mitigate these challenges. A mixed-use research methodology combining archival research, field surveys, stakeholder interviews, and case studies is employed to gather comprehensive data and insights. The findings reveal a complex interplay of socio-economic, political, and institutional factors shaping the preservation landscape in Africa, including issues related to funding, governance, community engagement, and capacity building. The paper concludes by highlighting the urgent need for coordinated efforts among government agencies, heritage organizations, local communities, and international stakeholders to address the identified challenges and develop sustainable preservation strategies. Recommendations are provided for enhancing legal frameworks, promoting community involvement, fostering public awareness, and mobilizing resources to safeguard Africa's rich architectural heritage for future generations.

Keywords: African architectural heritage, preservation challenges, preservation strategies, factors

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Authors: Fangwen Ni, Hongpeng Xu

Abstract:

When disasters occur, it is important to provide temporary shelters to protect victims from their environment and to comfort them with privacy and dignity. However, the commonly used shelters like tents and shanties can not ensure a comfortable condition. Furthermore, the demand for more energy and less pollution has become a major challenge. Focusing on the sustainable of temporary shelters, this study intends to clarify the essential role of temporary shelters before the reconstruction work is done. The paper also identifies the main problems from three aspects including spatial layout, thermal comfort and utilization of passive technology. Moreover, it expounds the passive strategies of ecological design by case study and simulation. It is found that the living condition of shelters can be improved from the perspective of architectural space, ventilation theory and construction techniques. Regardless of being temporary, these shelters are crucial elements in emergency situations and should be taken more seriously.

Keywords: architectural space, construction technique, sustainable strategy, temporary shelter

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

Abstract:

Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

Procedia PDF Downloads 128