Search results for: streaming analytics

Authors: Shohoni Mahabub

Abstract:

Health Information Systems (HIS) are essential to contemporary healthcare; nonetheless, they frequently encounter challenges such as data fragmentation, inefficiencies, and an absence of real-time analytics. The advent of machine learning (ML) and artificial intelligence (AI) provides a revolutionary potential to address these difficulties via AI-driven data fusion ecosystems. These ecosystems integrate many health data sources, including electronic health records (EHRs), wearable devices, and genetic data, with sophisticated machine learning techniques such as natural language processing (NLP) and predictive analytics to produce actionable insights. Through the integration of strong data intake layers, secure interoperability protocols, and privacy-preserving models, these ecosystems provide individualized treatment, early illness diagnosis, and enhanced operational efficiency. This paradigm change enhances clinical decision-making and rectifies systemic inefficiencies in healthcare delivery. Nonetheless, adoption presents problems such as data privacy concerns, ethical considerations, and scalability constraints. The study examines options such as federated learning for safe, decentralized data sharing, explainable AI for transparency, and cloud-based infrastructure for scalability to address these issues. These ecosystems aim to address health equity disparities, particularly in resource-limited environments, and improve public health surveillance, notably in pandemic response initiatives. This article emphasizes the revolutionary potential of AI-driven data fusion ecosystems in redefining Health Information Systems by providing an implementation roadmap and showcasing successful deployment case studies. The suggested method promotes a cooperative initiative among legislators, healthcare professionals, and technology to establish a cohesive, efficient, and patient-centric healthcare model.

Keywords: AI-powered healthcare systems, data fusion ecosystem, predictive analytics, digital health interoperability

Procedia PDF Downloads 15

Authors: Phil P. Wagner

Abstract:

There is an overwhelming amount of athlete-centric information available for sport practitioners in this era of tech and big data, but protocols in athletic rehabilitation remain arbitrary. It is a common assumption that the rate at which tissue heals amongst individuals is the same; yielding protocols that are entirely time-based. Progressing athletes through rehab programs that lack individualization can potentially expose athletes to stimuli they are not prepared for or unnecessarily lengthen their recovery period. A 7-year aggregated and anonymous database was used to develop reliable and valid assessments to measure athletic resilience. Each assessment utilizes force plate technology with proprietary protocols and analysis to provide key thresholds for injury risk and recovery. Using a T score to analyze movement qualities, much like the Z score used for bone density from a Dexa scan, specific prescriptions are provided to mitigate the athlete’s inherent injury risk. In addition to obliging to surgical clearance, practitioners must put in place a clearance protocol guided by standardized assessments and achievement in strength thresholds. In order to truly hold individuals accountable (practitioners, athletic trainers, performance coaches, etc.), success in improving pre-defined key performance indicators must be frequently assessed and analyzed.

Keywords: analytics, athlete rehabilitation, athlete resilience, injury prediction, injury prevention

Procedia PDF Downloads 229

Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

Abstract:

Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

Procedia PDF Downloads 197

Authors: Mirza Mujtaba Baig

Abstract:

Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

Procedia PDF Downloads 120

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

Abstract:

The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Adeniyi Onasanya, Maher Elshakankiri

Abstract:

In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

Keywords: IoT, smart health care system, business analytics, (wireless) sensor network, cancer care services, cloud services

Procedia PDF Downloads 178

Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

Abstract:

The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

Procedia PDF Downloads 68

Authors: Pedro G. Rodrigues, Maria J. Bárrios, Sara A. Ambrósio

Abstract:

The digitalisation of health is a profoundly transformative economic, political, and social process. As is often the case, such processes need to be carefully managed if misunderstandings, policy misalignments, or outright conflicts between the government and a wide gamut of stakeholders with competing interests are to be avoided. Thus, ensuring open lines of communication where all parties know what each other’s concerns are is key to good governance, as well as efficient and effective policymaking. This project aims to make a small but still significant contribution in this regard in that we seek to determine the extent to which health managers’ perceptions of what is a priority for digital health in Portugal are aligned with official policy perspectives. By applying state-of-the-art artificial intelligence technology first to the indexed literature on digital health and then to a set of official policy documents on the same topic, followed by a survey directed at health managers working in public and private hospitals in Portugal, we obtain two priority rankings that, when compared, will allow us to produce a synthesis and toolkit on digital health policy in Portugal, with a view to identifying areas of policy convergence and divergence. This project is also particularly peculiar in the sense that sophisticated digital methods related to text analytics are employed to study good governance aspects of digitalisation applied to health care.

Keywords: digital health, health informatics, text analytics, governance, natural language understanding

Procedia PDF Downloads 66

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

Abstract:

Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

Procedia PDF Downloads 67

Authors: Ismene Ithai Bras-Ruiz

Abstract:

Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

Procedia PDF Downloads 128

Authors: Elena Tkachenko, Vladimir Kokh, Alina Osipenko, Vladislav Surkov

Abstract:

The modern period of development of Russian economy is fraught with a number of problems related to limitations in the use of traditional planning and financial management tools. Restrictions in the use of foreign software when performing an order of the Russian Government, on the one hand, and sanctions limiting the support of the major ERP and MRP II systems in the Russian Federation, on the other hand, entail the necessity to appeal to the basics of developing budgeting and analysis systems for industrial enterprises. Thus, cost calculation theory becomes the theoretical foundation for the development of industrial cost management systems. Based on the foregoing, it would be fair to make an assumption that the development of a working managerial accounting model on an industrial enterprise using an automated enterprise resource management system should rest upon the concept of the inevitability of alterations of business processes. On the other hand, optimized business processes make the architecture of financial analytics more transparent and permit the use of all the benefits of data cubes. The metrics and indicator slices provide online assessment of the state of key business processes at a given moment of time, which improves the quality of managerial decisions considerably. Therefore, the bilateral sanctions situation boosted the development of corporate business analytics and took industrial companies to the next level of understanding of business processes.

Keywords: cost culculation, ERP, OLAP, modern Russian economy

Procedia PDF Downloads 223

Authors: Aleksandra Tomic

Abstract:

According to reliable sources, 4% of all websites is devoted to pornographic material, yet these estimates are often reported to be much higher. The largest internet pornography streaming website reports 21.2 billion visits in 2015 only. Considering the ubiquity of online pornography and the frequency of use, it is necessary to examine its potential influence on the construal of the sexual act and the roles of participants. Apart from the verbal and physical interactions in the pornographic movies themselves, the language in the titles of movies has the power to frame the sexual intercourse. In this study, Critical Discourse Analysis and corpus linguistics approaches will be used to examine the way the sexual intercourse and the roles of the participants are ideologically construed and perpetuated in the Internet pornography discourse. To this end, the study will explore the association between the specific morphosyntactic aspects of the references to performers of both genders, the person and the thematic role, and the gender of referred performer in the corpus of online pornographic movie titles. Distinctive collexeme analysis will be conducted to uncover possible associations between for gender of the performer denoted by the linguistic expression, and the person and thematic role assigned to it in the titles of online pornography movies. Initial results of the chi-square procedure performed on a sample of 295 online pornography movie titles on the largest pornography streaming website ‘Pornhub’ yielded significant results. The use of the three person categories was not equally distributed between genders, X2 (2, N = 106) = 32.52, p < 0.001, with female performers being referred to in the third person in 71.7% of the instances, and speaking in the first person 20.8% of the time, whereas male performers spoke in the first person 68% of the time, and were referred to in the third person in 17% of the instances. Moreover, there was a gender disparity in the assignment of thematic roles, with linguistic expressions for women being assigned the Patient role and men the Agent role in 58.8% of the cases, whereas the roles were reversed in 41.2% of the instances, X2 (1, N = 262) = 8.07633, p < 0.005. The results are discussed in terms of the ideologies surrounding female and male sexuality in the pornography discourse. Potential patterns of power imbalance, objectification, and discrimination are highlighted. Finally, the evidence from psycholinguistic studies on the influence of the language structure on event construal is related to the results of the study.

Keywords: corpus linguistics, gender studies, pornography, thematic roles

Procedia PDF Downloads 191

Authors: Muhammad Saad Aslam

Abstract:

In 2021, our organization has launched our proprietary mobile App i.e. Farm Intelligence platform, an industrial-first precision agriculture solution, to Pakistan. It was piloted at 47 locations (spanning around 1,200 hectares of land), addressing growers’ pain points by bringing the benefits of precision agriculture to their doorsteps. This year, we have extended its reach by more than 10 times (nearly 130,000 hectares of land) in almost 600 locations across the country. The project team selected highly infested areas to set up traps, which then enabled the sales team to initiate evidence-based conversations with the grower community about preventive crop protection products that includes pesticides and insecticides. Mega farmer meeting field visits and demonstrations plots coupled with extensive marketing activities, were setup to include farmer community. With the help of App real-time pest monitoring (using heat maps and infestation prediction through predictive analytics) we have equipped our growers with on spot insights that will help them optimize pesticide applications. Heat maps allow growers to identify infestation hot spots to fine-tune pesticide delivery, while predictive analytics enable preventive application of pesticides before the situation escalates. Ultimately, they empower growers to keep their crops safe for a healthy harvest.

Keywords: precision pest management, precision agriculture, real time pest tracking, pest forecasting

Procedia PDF Downloads 92

Authors: Maryam Ziaee, Himanshu Shee, Amrik Sohal

Abstract:

Big Data Analytics (BDA) in supply chain management has recently drawn the attention of academics and practitioners. Big data refers to a massive amount of data from different sources, in different formats, generated at high speed through transactions in business environments and supply chain networks. Traditional statistical tools and techniques find it difficult to analyse this massive data. BDA can assist organisations to capture, store, and analyse data specifically in the field of supply chain. Currently, there is a paucity of research on BDA in the pharmaceutical supply chain context. In this research, the Australian pharmaceutical supply chain was selected as the case study. This industry is highly significant since the right medicine must reach the right patients, at the right time, in right quantity, in good condition, and at the right price to save lives. However, drug shortages remain a substantial problem for hospitals across Australia with implications on patient care, staff resourcing, and expenditure. Furthermore, a massive volume and variety of data is generated at fast speed from multiple sources in pharmaceutical supply chain, which needs to be captured and analysed to benefit operational decisions at every stage of supply chain processes. As the pharmaceutical industry lags behind other industries in using BDA, it raises the question of whether the use of BDA can improve transparency among pharmaceutical supply chain by enabling the partners to make informed-decisions across their operational activities. This presentation explores the impacts of BDA on supply chain management. An exploratory qualitative approach was adopted to analyse data collected through interviews. This study also explores the BDA potential in the whole pharmaceutical supply chain rather than focusing on a single entity. Twenty semi-structured interviews were undertaken with top managers in fifteen organisations (five pharmaceutical manufacturers, five wholesalers/distributors, and five public hospital pharmacies) to investigate their views on the use of BDA. The findings revealed that BDA can enable pharmaceutical entities to have improved visibility over the whole supply chain and also the market; it enables entities, especially manufacturers, to monitor consumption and the demand rate in real-time and make accurate demand forecasts which reduce drug shortages. Timely and precise decision-making can allow the entities to source and manage their stocks more effectively. This can likely address the drug demand at hospitals and respond to unanticipated issues such as drug shortages. Earlier studies explore BDA in the context of clinical healthcare; however, this presentation investigates the benefits of BDA in the Australian pharmaceutical supply chain. Furthermore, this research enhances managers’ insight into the potentials of BDA at every stage of supply chain processes and helps to improve decision-making in their supply chain operations. The findings will turn the rhetoric of data-driven decision into a reality where the managers may opt for analytics for improved decision-making in the supply chain processes.

Keywords: big data analytics, data-driven decision, pharmaceutical industry, supply chain management

Procedia PDF Downloads 108

Authors: Jenny K. Ubaque, Edward P. Guillen, Juan S. Solórzano, Leonardo J. Ramírez

Abstract:

The health care must be a right for people around the world, but in order to guarantee the access to all, it is necessary to overcome geographical barriers. Telemedicine take advantage of Information Communication Technologies to deploy health care services around the world. To achieve those goals, it is necessary to use existing last mile solution to create access for home users, which is why is necessary to establish the channel characteristics for those kinds of services. This paper presents an analysis of network performance of last mile solution for the use of IPTV broadcasting with the application of streaming for telemedicine apps.

Keywords: telemedicine, IPTV, GPON, ADSL2+, coaxial, jumbogram

Procedia PDF Downloads 368

Authors: Somnath Bhattacharyya

Abstract:

The mobile adsorbed surface charge on hydrophobic surfaces can modify the velocity slip condition as well as create a Marangoni stress at the interface. The functionalized hydrophobic walls of micro/nanopores, e.g., graphene nanochannels, may possess physio-sorbed ions. The lateral mobility of the physisorbed absorbed ions creates a friction force as well as an electric force, leading to a modification in the velocity slip condition at the hydrophobic surface. In addition, the non-uniform distribution of these surface ions creates a surface tension gradient, leading to a Marangoni stress. The impact of the mobile surface charge on streaming potential and electrochemical energy conversion efficiency in a pressure-driven flow of ionized liquid through the nanopore is addressed. Also, enhanced electro-osmotic flow through the hydrophobic nanochannel is also analyzed. The mean-filed electrokinetic model is modified to take into account the short-range non-electrostatic steric interactions and the long-range Coulomb correlations. The steric interaction is modeled by considering the ions as charged hard spheres of finite radius suspended in the electrolyte medium. The electrochemical potential is modified by including the volume exclusion effect, which is modeled based on the BMCSL equation of state. The electrostatic correlation is accounted for in the ionic self-energy. The extremal of the self-energy leads to a fourth-order Poisson equation for the electric field. The ion transport is governed by the modified Nernst-Planck equation, which includes the ion steric interactions; born force arises due to the spatial variation of the dielectric permittivity and the dielectrophoretic force on the hydrated ions. This ion transport equation is coupled with the Navier-Stokes equation describing the flow of the ionized fluid and the 3fourth-order Poisson equation for the electric field. We numerically solve the coupled set of nonlinear governing equations along with the prescribed boundary conditions by adopting a control volume approach over a staggered grid arrangement. In the staggered grid arrangements, velocity components are stored on the midpoint of the cell faces to which they are normal, whereas the remaining scalar variables are stored at the center of each cell. The convection and electromigration terms are discretized at each interface of the control volumes using the total variation diminishing (TVD) approach to capture the strong convection resulting from the highly enhanced fluid flow due to the modified model. In order to link pressure to the continuity equation, we adopt a pressure correction-based iterative SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm, in which the discretized continuity equation is converted to a Poisson equation involving pressure correction terms. Our results show that the physisorbed ions on a hydrophobic surface create an enhanced slip velocity when streaming potential, which enhances the convection current. However, the electroosmotic flow attenuates due to the mobile surface ions.

Keywords: microfluidics, electroosmosis, streaming potential, electrostatic correlation, finite sized ions

Procedia PDF Downloads 72

Authors: Gregory D. Saxton

Abstract:

Networks are everywhere: lunch ties among co-workers, golfing partnerships among employees, interlocking board-of-director connections, Facebook friendship ties, etc. Each network varies in terms of its structure -its size, how inter-connected network members are, and the prevalence of sub-groups and cliques. At the same time, within any given network, some network members will have a more important, more central position on account of their greater number of connections or their capacity as “bridges” connecting members of different network cliques. The logic of network structure and position is at the heart of what is known as social network analysis, and this paper applies this logic to the study of the stock market. Using an array of data analytics and machine learning tools, this study will examine 17 million Twitter messages discussing the stocks of the firms in the S&P 1,500 index in 2018. Each of these 1,500 stocks has a distinct Twitter discussion network that varies in terms of core network characteristics such as size, density, influence, norms and values, level of activity, and embedded resources. The study’s core proposition is that the ultimate effect of any market-relevant information is contingent on the characteristics of the network through which it flows. To test this proposition, this study operationalizes each of the core network characteristics and examines their influence on market reactions to 2018 quarterly earnings announcement events.

Keywords: data analytics, investor-to-investor communication, social network analysis, Twitter

Procedia PDF Downloads 123

Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

Abstract:

Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

Procedia PDF Downloads 522

Authors: Sara ElElimy, Samir Moustafa

Abstract:

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 140

Authors: Bongs Lainjo

Abstract:

The evolution and escalation of bike-sharing programs (BSP) continue unabated. Since the sixties, many countries have introduced different models and strategies of BSP. These include variations ranging from dockless models to electronic real-time monitoring systems. Reasons for using this BSP include recreation, errands, work, etc. And there is all indication that complex, and more innovative rider-friendly systems are yet to be introduced. The objective of this paper is to analyze current variables established by different operators and streamline them identifying the most compelling ones using analytics. Given the contents of available databases, there is a lack of uniformity and common standard on what is required and what is not. Two factors appear to be common: user type (registered and unregistered, and duration of each trip). This article uses historical data provided by one operator based in the greater Washington, District of Columbia, USA area. Several variables including categorical and continuous data types were screened. Eight out of 18 were considered acceptable and significantly contribute to determining a useful and reliable predictive model. Bike-sharing systems have become popular in recent years all around the world. Although this trend has resulted in many studies on public cycling systems, there have been few previous studies on the factors influencing public bicycle travel behavior. A bike-sharing system is a computer-controlled system in which individuals can borrow bikes for a fee or free for a limited period. This study has identified unprecedented useful, and pragmatic parameters required in improving BSP ridership dynamics.

Keywords: sharing program, historical data, parameters, ridership dynamics, trip duration

Procedia PDF Downloads 139

Authors: Kwaku Damoah

Abstract:

The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

Procedia PDF Downloads 63

Authors: Saahith M. S., Sivakami R.

Abstract:

In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

Procedia PDF Downloads 39

Authors: Samir Benzegane, Said Sadoudi, Mustapha Djeddou

Abstract:

In this paper, we present a software development of video streaming encryption for Device-to-Device (D2D) communications by using Hyperchaos-based Random Number Generator (HRNG) implemented in C#. The software implements and uses the proposed HRNG to generate key stream for encrypting and decrypting real-time video data. The used HRNG consists of Hyperchaos Lorenz system which produces four signal outputs taken as encryption keys. The generated keys are characterized by high quality randomness which is confirmed by passing standard NIST statistical tests. Security analysis of the proposed encryption scheme confirms its robustness against different attacks.

Keywords: hyperchaos Lorenz system, hyperchaos-based random number generator, D2D communications, C#

Procedia PDF Downloads 374

Authors: Kedar J. Mahadeshwar

Abstract:

Concept introduction: This paper talks about how an innovative cloud based analytics enabled solution that could address a major industry challenge that is approaching all of us globally faster than what one would think. The world of supply chain for drugs and devices is changing today at a rapid speed. In the US, the Drug Supply Chain Security Act (DSCSA) is a new law for Tracing, Verification and Serialization phasing in starting Jan 1, 2015 for manufacturers, repackagers, wholesalers and pharmacies / clinics. Similarly we are seeing pressures building up in Europe, China and many countries that would require an absolute traceability of every drug and device end to end. Companies (both manufacturers and distributors) can use this opportunity not only to be compliant but to differentiate themselves over competition. And moreover a country such as UAE can be the leader in coming up with a global solution that brings innovation in this industry. Problem definition and timing: The problem of counterfeit drug market, recognized by FDA, causes billions of dollars loss every year. Even in UAE, the concerns over prevalence of counterfeit drugs, which enter through ports such as Dubai remains a big concern, as per UAE pharma and healthcare report, Q1 2015. Distribution of drugs and devices involves multiple processes and systems that do not talk to each other. Consumer confidence is at risk due to this lack of traceability and any leading provider is at risk of losing its reputation. Globally there is an increasing pressure by government and regulatory bodies to trace serial numbers and lot numbers of every drug and medical devices throughout a supply chain. Though many of large corporations use some form of ERP (enterprise resource planning) software, it is far from having a capability to trace a lot and serial number beyond the enterprise and making this information easily available real time. Solution: The solution here talks about a service provider that allows all subscribers to take advantage of this service. The solution allows a service provider regardless of its physical location, to host this cloud based traceability and analytics solution of millions of distribution transactions that capture lots of each drug and device. The solution platform will capture a movement of every medical device and drug end to end from its manufacturer to a hospital or a doctor through a series of distributor or retail network. The platform also provides advanced analytics solution to do some intelligent reporting online. Why Dubai? Opportunity exists with huge investment done in Dubai healthcare city also with using technology and infrastructure to attract more FDI to provide such a service. UAE and countries similar will be facing this pressure from regulators globally in near future. But more interestingly, Dubai can attract such innovators/companies to run and host such a cloud based solution and become a hub of such traceability globally.

Keywords: cloud, pharmaceutical, supply chain, tracking

Procedia PDF Downloads 528

Authors: K. Aravinth, C. T. Vignesh

Abstract:

The usage of pulse tube cryocoolers is significantly increased mainly due to the advantage of the absence of moving parts. The underlying idea of this project is to optimize the design of pulse tube, regenerator, a resonator in cryocooler and analyzing the thermo-acoustic oscillations with respect to the design parameters. Computational Fluid Dynamic (CFD) model with time-dependent validation is done to predict its performance. The continuity, momentum, and energy equations are solved for various porous media regions. The effect of changing the geometries and orientation will be validated and investigated in performance. The pressure, temperature and velocity fields in the regenerator and pulse tube are evaluated. This optimized design performance results will be compared with the existing pulse tube cryocooler design. The sinusoidal behavior of cryocooler in acoustic streaming patterns in pulse tube cryocooler will also be evaluated.

Keywords: acoustics, cryogenics, design, optimization

Procedia PDF Downloads 176

Authors: Amanuel Berhanu Kesamo

Abstract:

This paper presents both circuit simulation and hardware implementation of a robot vehicle that can be either controlled manually via Bluetooth with video streaming or navigate autonomously to a target point by avoiding obstacles. In manual mode, the user controls the mobile robot using C# windows form interfaced via Bluetooth. The camera mounted on the robot is used to capture and send the real time video to the user. In autonomous mode, the robot plans the shortest path to the target point while avoiding obstacles along the way. Ultrasonic sensor is used for sensing the obstacle in its environment. An efficient path planning algorithm is implemented to navigate the robot along optimal route.

Keywords: Arduino Uno, autonomous, Bluetooth module, path planning, remote controlled robot, ultra sonic sensor

Procedia PDF Downloads 145

Authors: Rutusha Bhutada, Gaurav Chavan, Sarvesh Kasat, Varsha Mujumdar

Abstract:

This software system will be an Augmented Reality application designed to maximize the doctor’s productivity by providing tools to assist in automating the patient recognition and updating patient’s records using face and voice recognition features, which would otherwise have to be performed manually. By maximizing the doctor’s work efficiency and production, the application will meet the doctor’s needs while remaining easy to understand and use. More specifically, this application is designed to allow a doctor to manage his productive time in handling the patient without losing eye-contact with him and communicate with a group of other doctors for consultation, for in-place treatments through video streaming, as a video study. The system also contains a relational database containing a list of doctor, patient and display techniques.

Keywords: augmented reality, hand-held devices, head-mounted devices, marker based systems, speech recognition, face detection

Procedia PDF Downloads 436

Authors: F. Lazzeri, I. Reiter

Abstract:

Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

Procedia PDF Downloads 299

Authors: Stanis Koko Nyalongomo, Benjamin Mputela Bankanza, Moise Kisempa Mahungudi

Abstract:

Several areas in the Democratic Republic of Congo (DRC) experience serious problems of streaming and soil erosion. Erosion leads to degradation of soil health, and the three main causative factors of similar importance are deforestation, overgrazing, and land agricultural mismanagement. Degradation of soil health leads to a decrease in agricultural productivity and carbon dioxide (CO₂), and other greenhouse gas emissions. Agricultural productivity low, and sanitation-related diseases are a concern of a majority of DRC rural people -whose main livelihoods are conventional smallholder agriculture- due to degradation of agricultural soil health and prevalence of inappropriate sanitation in rural areas. Land management practices that increase soil carbon stocks on agricultural lands with practices including conservation agriculture and agroforestry do not only limit CO₂ emissions but also help prevent erosion while enhancing soil health and productivity. Promotion to adopt sustainable land management practices, especially conversion to well-managed agroforestry practices, is a necessity. This needs to be accompanied by incentives. Methods that incite smallholders to adopt practices that increase carbon stocks in agricultural lands and enhance soil health and productivity for social, economic, and environmental benefits, and give them the ability to get and use household dry toilets -included activities to inform and raise smallholder households awareness on the conversion of croplands to well-managed agroforestry systems through planting at least 20 fruit trees and/or acacias, soil carbon and practices that sequester it in soil and ecological sanitation; and offer smallholders technique and material supports and incentives under the form of dry toilets constructed for free for well-managed agroforestry implementation- were carried out to address problems of soil erosion as well as agricultural productivity and sanitation-related diseases. In 2018 and 2019, 19 of 23 targeted smallholder households expressed their satisfaction and converted their croplands to agroforestry through planting 374 trees, and each gotten 1 dry toilet constructed for free. Their neighbors expressed a willingness to participate in the project. Conversion to well-managed agroforestry practices offers many advantages to both farmers and the environment. The strategy of offering smallholders incentives for soil-friendly agricultural practices, especially well-managed agroforestry, is one of the solutions to prevent soil erosion. DRC rural people whose majority are smallholder households, need to be able to get and use dry toilets. So, dry toilets could be offered like incentives for well-managed agroforestry practices. Given the many advantages agroforestry and dry toilet can offer, recommendations are made for funding organizations to support such projects that promote the adoption of soil health practices.

Keywords: agroforestry, croplands, soil carbon, soil health

Procedia PDF Downloads 124

Authors: Arin Ghazarian, Cyril Rakovski

Abstract:

Differential privacy has become the leading technique to protect the privacy of individuals in a database while allowing useful analysis to be done and the results to be shared. It puts a guarantee on the amount of privacy loss in the worst-case scenario. Differential privacy is not a toggle between full privacy and zero privacy. It controls the tradeoff between the accuracy of the results and the privacy loss using a single key parameter called

Keywords: arrhythmia, cardiology, differential privacy, ECG, epsilon, medi-cal data, privacy preserving analytics, statistical databases

Procedia PDF Downloads 153