Search results for: startup data analytics

Authors: Pietro D'Ambrosio, Roberta D'Ambrosio

Abstract:

The upgrading of the architectural and urban heritage of the urban historic centers almost always involves the planning for the reuse and refunctionalization of the structures. Such interventions have complexities linked to the need to take into account the urban and social context in which the structure and its intrinsic characteristics such as historical and artistic value are inserted. To these, of course, we have to add the need to make a preliminary estimate of recovery costs and more generally to assess the economic and financial sustainability of the whole project of re-socialization. Particular difficulties are encountered during the pre-assessment of costs since it is often impossible to perform analytical surveys and structural tests for both structural conditions and obvious cost and time constraints. The methodology proposed in this work, based on a multidisciplinary and data-driven approach, is aimed at obtaining, at very low cost, reasonably priced economic evaluations of the interventions to be carried out. In addition, the specific features of the approach used, derived from the predictive analysis techniques typically applied in complex IT domains (big data analytics), allow to obtain as a result indirectly the evaluation process of a shared database that can be used on a generalized basis to estimate such other projects. This makes the methodology particularly indicated in those cases where it is expected to intervene massively across entire areas of historical city centers. The methodology has been partially tested during a study aimed at assessing the feasibility of a project for the reuse of the monumental complex of San Massimo, located in the historic center of Salerno, and is being further investigated.

Keywords: evaluation, methodology, restoration, reuse

Procedia PDF Downloads 187

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: Carl-Philipp Valentin Beichert, Marcel Seger

Abstract:

Digital transformation and climate change require small and medium-sized enterprises (SME) to rethink their way of doing business. Inter-firm collaboration is recognized as helpful means of promoting innovation and competitiveness. In this context, collaborations with start-ups offer valuable opportunities through their innovative products, services, and business models. SMEs, and in particular German craft enterprises, play an important role in the country’s society and economy. Companies in this heterogeneous economic sector have unique characteristics and are limited in their ability to innovate due to their small size and lack of resources. Collaborating with start-ups could help to overcome these shortcomings. To investigate how collaborations emerge and what factors are decisive to successfully drive collaboration, we apply an explorative, qualitative research design. A sample of ten case studies was selected, with the collaboration between a start-up and a craft enterprise forming the unit of analysis. Semi-structured interviews with 20 company representatives allow for a two-sided perspective on the respective collaboration. The interview data is enriched by publicly available data and three expert interviews. As a result, objectives, initiation practices, applied collaboration types, barriers, as well as key success factors could be identified. The results indicate a three-phase collaboration process comprising an initiation, concept, and partner phase (ICP). The ICP framework proposed accordingly highlights the success factors (personal fit, communication, expertise, structure, network) for craft enterprises and start-ups for each collaboration phase. The role of a mediator in the start-up company, with strong expertise in the respective craft sector, is considered an important lever for overcoming barriers such as cultural and communication differences. The ICP framework thus provides promising directions for further research and can help practitioners establish successful collaborations.

Keywords: open innovation, SME, craft businesses, startup collaboration, qualitative research

Procedia PDF Downloads 97

Authors: Chia-Chen Wei, Pack Hsieh, Jeffrey Chen

Abstract:

Surface Mount Technology (SMT) is widely used in the area of the electronic assembly in which the electronic components are mounted to the surface of the printed circuit board (PCB). Most of the defects in the SMT process are mainly related to the quality of solder paste printing. These defects lead to considerable manufacturing costs in the electronics assembly industry. Therefore, the solder paste inspection (SPI) machine for controlling and monitoring the amount of solder paste printing has become an important part of the production process. So far, the setting of the SPI threshold is based on statistical analysis and experts’ experiences to determine the appropriate threshold settings. Because the production data are not normal distribution and there are various variations in the production processes, defects related to solder paste printing still occur. In order to solve this problem, this paper proposes an online machine learning algorithm, called the automatic threshold adjustment (ATA) algorithm, and closed-loop architecture in the SMT process to determine the best threshold settings. Simulation experiments prove that our proposed threshold settings improve the accuracy from 99.85% to 100%.

Keywords: big data analytics, Industry 4.0, SPI threshold setting, surface mount technology

Procedia PDF Downloads 117

Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

Abstract:

The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 360

Authors: Nur Sahver Uslu, Ali̇ Hakan Büyüklü

Abstract:

Maintenance, Repair, and Overhaul (MRO) costs are significant in the aviation industry. On the other hand, companies that provide MRO services to the aviation industry but are not dominant in the sector, need to determine the right strategies for sustainable profitability in a competitive environment. This study examined the operational real data of a small medium enterprise (SME) MRO company where analytical methods are not widely applied. The company's customers were divided into two categories: airline companies and non-airline companies, and the variables that best explained profitability were analyzed with Logistic Regression for each category and the results were compared. First, data reduction was applied to the transformed variables that went through the data cleaning and preparation stages, and the variables to be included in the model were decided. The misclassification rates for the logistic regression results concerning both customer categories are similar, indicating consistent model performance across different segments. Less profit margin is obtained from airline customers, which can be explained by the variables part description, time to quotation (TTQ), turnaround time (TAT), manager, part cost, and labour cost. The higher profit margin obtained from non-airline customers is explained only by the variables part description, part cost, and labour cost. Based on the two models, it can be stated that it is significantly more challenging for the MRO company, which is the subject of our study, to achieve profitability from Airline customers. While operational processes and organizational structure also affect the profit from airline customers, only the type of parts and costs determine the profit for non-airlines.

Keywords: aircraft, aircraft components, aviation, data analytics, data science, gini index, maintenance, repair, and overhaul, MRO, logistic regression, profit, variable clustering, variable reduction

Procedia PDF Downloads 35

Authors: Denis Özdemir, Max Senges

Abstract:

There are many examples of AI-supported apps for better health and wellbeing. Generally, these applications help people to achieve their goals based on scientific research and input data. Still, they do not always explain how those three are related, e.g. by making implicit assumptions about goals that hold for many but not for all. We present a seven-step method for designing health and wellbeing AIs considering goal setting, measurable results, real-time indicators, analytics, visual representations, communication, and feedback. It can help engineers as guidance in developing apps, recommendation algorithms, and interfaces that support humans in their decision-making without patronization. To illustrate the method, we create a recommender AI for tiny wellbeing habits and run a small case study, including a survey. From the results, we infer how people perceive the relationship between them and the AI and to what extent it helps them to achieve their goals. We review our seven-step engineering method and suggest modifications for the next iteration.

Keywords: recommender systems, natural language processing, health apps, engineering methods

Procedia PDF Downloads 166

Authors: Kenny Raharjo, Ramon Lawrence

Abstract:

Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.

Keywords: game design, multi-arm bandit, design exploration and data mining, player metric optimization and analytics

Procedia PDF Downloads 511

Authors: S. C. Santos, S. R. Dionísio, A. L. D. De Andrade, L. R. Roque, A. C. Da Costa, J. L. Ienczak

Abstract:

In this work, two fermentations at different temperatures (25 and 30 ºC), with cell recycling, were accomplished to produce ethanol, using a mix of commercial substrates, xylose (70%) and glucose (30%), as organic source for Scheffersomyces stipitis. Five consecutive fermentations of 80 g L-1 (1º, 2º and 3º recycles), 96 g L-1 (4º recycle) and 120 g L-1 (5º recycle)reduced sugars led to a final maximum ethanol concentration of 17.2 and 34.5 g L-1, at 25 and 30 ºC, respectively. Glucose was the preferred substrate; moreover xylose startup degradation was initiated after a remaining glucose presence in the medium. Results showed that yeast acid treatment, performed before each cycle, provided improvements on cell viability, accompanied by ethanol productivity of 2.16 g L-1 h-1 at 30 ºC. A maximum 36% of xylose was retained in the fermentation medium and after five-cycle fermentation an ethanol yield of 0.43 g ethanol/g sugars was observed. S. stipitis fermentation capacity and tolerance showed better results at 30 ºC with 83.4% of theoretical yield referenced on initial biomass.

Keywords: 5-carbon sugar, cell recycling fermenter, mixed sugars, xylose-fermenting yeast

Procedia PDF Downloads 419

Authors: Michael Aupetit, Mahmoud Al-ismail, Khaled Mohamed

Abstract:

Cancer is a major public health problem all over the world. Breast cancer has the highest incidence rate over all cancers for women in Qatar making its study a top priority of the country. Human cancer is a dynamic disease that develops over an extended period through the accumulation of a series of genetic alterations. A Darwinian process drives the tumor cells toward higher malignancy growing the branches of a progression tree in the space of genes expression. Although it is not possible to track these genetic alterations dynamically for one patient, it is possible to reconstruct the progression tree from the aggregation of thousands of tumor cells’ genetic profiles from thousands of different patients at different stages of the disease. Analyzing the progression tree is a way to detect pivotal molecular events that drive the malignant evolution and to provide a guide for the development of cancer diagnostics, prognostics and targeted therapeutics. In this work we present the development of a Visual Analytic web platform CanVis enabling users to upload gene-expression data and analyze their progression tree. The server computes the progression tree based on state-of-the-art techniques and allows an interactive visual exploration of this tree and the gene-expression data along its branching structure helping to discover potential driver genes.

Keywords: breast cancer, progression tree, visual analytics, web platform

Procedia PDF Downloads 419

Authors: Sulemana Ibrahim

Abstract:

Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

Procedia PDF Downloads 63

Authors: Aiswarya Gopal, Kamal Bijlani, Vinoth Rengaraj, R. Jayakrishnan

Abstract:

In the business world, the term “startup” is frequently ringing the bell with the high frequency of young ventures. The main dilemma of startups is the unsuccessful management of the unique risks that have to be confronted in the present world of competition and technology. This research work tried to bring out a game based methodology to improve enough real-world experience among entrepreneurs as well as management students to handle risks and challenges in the field. The game will provide experience to the player to overcome challenges like market problems, running out of cash, poor management, and product problems which can be resolved by a proper strategic approach in the entrepreneurship world. The proposed serious game works on the life cycle of a new software enterprise where the entrepreneur moves from the planning stage to secured financial stage, laying down the basic business structure, and initiates the operations ensuring the increment in confidence level of the player.

Keywords: business model, game based learning, poor management, start up

Procedia PDF Downloads 477

Authors: Innocent Uzougbo Onwuegbuzie, Siene Elizabeth Eke

Abstract:

This study investigates cybersecurity challenges and solutions in managing Information and Communication Technology (ICT) at the Federal Polytechnic, Ado-Ekiti, South-West Nigeria. The rapid evolution of ICT has revolutionized organizational operations and impacted various sectors, including education, healthcare, and finance. While ICT advancements facilitate seamless communication, complex data analytics, and strategic decision-making, they also introduce significant cybersecurity risks such as data breaches, ransomware, and other malicious attacks. These threats jeopardize the confidentiality, integrity, and availability of information systems, necessitating robust cybersecurity measures. The primary aim of this research is to identify prevalent cybersecurity challenges in ICT management, evaluate their impact on the institution's operations, and assess the effectiveness of current cybersecurity solutions. Adopting a quantitative research approach, data was collected through surveys and structured questionnaires from students, staff, and IT professionals at the Federal Polytechnic, Ado-Ekiti. The findings underscore the critical need for continuous investment in cybersecurity technologies, employee and student training, and regulatory compliance to mitigate evolving cyber threats. This research contributes to bridging the knowledge gap in cybersecurity management and provides valuable insights into effective strategies and technologies for safeguarding ICT systems in educational institutions. The study's objectives are to enhance the security posture of the Federal Polytechnic, Ado-Ekiti, in an increasingly digital world by identifying and addressing the cybersecurity challenges faced by its ICT management.

Keywords: cybersecurity challenges, cyber threat mitigation, federal polytechnic Ado-Ekiti, ICT management

Procedia PDF Downloads 42

Authors: Rishi Agarwal

Abstract:

Digital twins are emerging as a strong technology used to imitate and monitor physical objects digitally in real time across sectors. It is not only dealing with the digital space, but it is also actuating responses in the physical space in response to the digital space processing like storage, modeling, learning, simulation, and prediction. This paper explores the application of digital twins for enhancing physical security in retail stores. The retail sector still relies on outdated physical security practices like manual monitoring and metal detectors, which are insufficient for modern needs. There is a lack of real-time data and system integration, leading to ineffective emergency response and preventative measures. As retail automation increases, new digital frameworks must control safety without human intervention. To address this, the paper proposes implementing an intelligent digital twin framework. This collects diverse data streams from in-store sensors, surveillance, external sources, and customer devices and then Advanced analytics and simulations enable real-time monitoring, incident prediction, automated emergency procedures, and stakeholder coordination. Overall, the digital twin improves physical security through automation, adaptability, and comprehensive data sharing. The paper also analyzes the pros and cons of implementation of this technology through an Emerging Technology Analysis Canvas that analyzes different aspects of this technology through both narrow and wide lenses to help decision makers in their decision of implementing this technology. On a broader scale, this showcases the value of digital twins in transforming legacy systems across sectors and how data sharing can create a safer world for both retail store customers and owners.

Keywords: digital twin, retail store safety, digital twin in retail, digital twin for physical safety

Procedia PDF Downloads 73

Authors: Michelangelo Sofo, Giuseppe Labianca

Abstract:

In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

Procedia PDF Downloads 26

Authors: Ha Thi Nguyen, Guangya Yang, Arne Hejde Nielsen, Peter Højgaard Jensen

Abstract:

Automatic voltage regulator (AVR) plays an important role in volt/var control of synchronous condenser (SC) in power systems. Test AVR performance in steady-state and dynamic conditions in real grid is expensive, low efficiency, and hard to achieve. To address this issue, we implement hardware-in-the-loop (HiL) test for the AVR of SC to test the steady-state and dynamic performances of AVR in different operating conditions. Startup procedure of the system and voltage set point changes are studied to evaluate the AVR hardware response. Overexcitation, underexcitation, and AVR set point loss are tested to compare the performance of SC with the AVR hardware and that of simulation. The comparative results demonstrate how AVR will work in a real system. The results show HiL test is an effective approach for testing devices before deployment and is able to parameterize the controller with lower cost, higher efficiency, and more flexibility.

Keywords: automatic voltage regulator, hardware-in-the-loop, synchronous condenser, real time digital simulator

Procedia PDF Downloads 253

Authors: Jacky Liu

Abstract:

This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

Procedia PDF Downloads 102

Authors: Shailaja Grover, Purvi Tiwari, Vigneshwaran S. R., U. Dinesh Kumar

Abstract:

In India, grapes are one of the most important horticulture crops. Grapes are most vulnerable to downy mildew, which is one of the most devasting diseases. In the absence of a precise weather-based advisory system, farmers spray pesticides on their crops extensively. There are two main challenges associated with using these pesticides. Firstly, most of these sprays were panic sprays, which could have been avoided. Second, farmers use more expensive "Preventive and Eradicate" chemicals than "Systemic, Curative and Anti-sporulate" chemicals. When these chemicals are used indiscriminately, they can enter the fruit and cause health problems such as cancer. This paper utilizes decision trees and predictive modeling techniques to provide grape farmers with customized advice on grape disease management. This model is expected to reduce the overall use of chemicals by approximately 50% and the cost by around 70%. Most of the grapes produced will have relatively low residue levels of pesticides, i.e., below the permissible level.

Keywords: analytics in agriculture, downy mildew, weather based advisory, decision tree, predictive modelling

Procedia PDF Downloads 74

Authors: Aditya Patil, Arun Patil, Vaishali Patil, Sudhir Chitnis, Anjum Patel

Abstract:

Rapid growth has given e-commerce platforms a lot of client behavior and spending data. To maximize their strategy, businesses must understand how customers utilize online shopping platforms and what influences their purchases. Our research focuses on e-commerce user behavior and purchasing trends. This extensive study examines spending and user behavior. Regression and grouping disclose relevant data from the dataset. We can understand user spending trends via multilevel regression. We can analyze how pricing, user demographics, and product categories affect customer purchase decisions with this technique. Clustering groups consumers by spending. Important information was found. Purchase habits vary by user group. Our analysis illuminates the complex world of e-commerce consumer behavior and purchase trends. Understanding user behavior helps create effective e-commerce marketing strategies. This market can benefit from K-means clustering. This study focuses on tailoring strategies to user groups and improving product and price effectiveness. Customer buying behaviors across categories were shown via K-means clusters. Average spending is highest in Cluster 4 and lowest in Cluster 3. Clothing is less popular than gadgets and appliances around the holidays. Cluster spending distribution is examined using average variables. Our research enhances e-commerce analytics. Companies can improve customer service and decision-making with this data.

Keywords: e-commerce, regression, clustering, k-means

Procedia PDF Downloads 23

Authors: Huda Al-Shuaily, E. M. Lacap

Abstract:

Higher Education is one of the most essential fundamentals for the advancement and progress of a country. It demands to be as accessible as possible and as comprehensive as it can be reached. In this paper, we succeeded to expand the accessibility and delivery of higher education using an Open Educational Resources (OER), a freely accessible, openly licensed documents, and media for teaching and learning. This study creates a comparative design of student’s academic performance on the course Introduction to Database and student engagement to the virtual learning environment (VLE). The study was done in two successive semesters - one without using the OER and the other is using OER. In the study, we established that there is a significant increase in student’s engagement in VLE in the latter semester compared to the former. By using the latter semester’s data, we manage to show that the student’s engagement has a positive impact on students’ academic performance. Moreso, after clustering their academic performance, the impact is seen higher for students who are low performing. The results show that these engagements can be used to potentially predict the learning styles of the student with a high degree of precision.

Keywords: EDM, learning analytics, moodle, OER, student-engagement

Procedia PDF Downloads 339

Authors: C. J. Rossouw, T. I. van Niekerk

Abstract:

The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

Procedia PDF Downloads 88

Authors: Gaurav Kumar Sinha

Abstract:

The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

Procedia PDF Downloads 36

Authors: Lauren B. Birney

Abstract:

Summer STEM Institute for Middle and High School Students at Pace University The STEM Collaboratory NYC® Summer Fellows Institute takes place on Pace University’s New York City campus during July and provides the following key features for all participants: (i) individual meetings with Pace faculty to discuss and refine future educational goals; (ii) mentorship, guidance, and new friendships with program leaders; and (iii) guest lectures from professionals in STEM disciplines and businesses. The Summer STEM Institute allows middle school and high school students to work in teams to conceptualize, develop, and build native mobile applications that teach and reinforce skills in the sciences and mathematics. These workshops enhance students’STEM problem solving techniques and teach advanced methods of computer science and engineering. Topics include: big data and analytics at the Big Data lab at Seidenberg, Data Science focused on social and environmental advancement and betterment; Natural Disasters and their Societal Influences; Algal Blooms and Environmental Impacts; Green CitiesNYC; STEM jobs and growth opportunities for the future; renew able energy and sustainable infrastructure; and climate and the economy. In order to better align the existing Summer STEM, Institute with the CCERS model and expand the overall network, Pace is actively recruiting new content area specialists from STEM industries and private sector enterprises to participate in an enhanced summer institute in order to1) nurture student progress and connect summer learning to school year curriculum, 2) increase peer-to-peer collaboration amongst STEM professionals and private sector technologists, and 3) develop long term funding and sponsorship opportunities for corporate sector partners to support CCERS schools and programs directly.

Keywords: environmental restoration science, citizen science, data science, STEM

Procedia PDF Downloads 86

Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

Abstract:

The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

Procedia PDF Downloads 131

Authors: Wildan Maulana, Machfudz Sa'idi

Abstract:

Yogyakarta is a city with the most students in Indonesia, more than 250 thousand students living in Yogyakarta and more than 140 universities in Yogyakarta. Therefore, Yogyakarta is a very strategic place for the culinary business. Food is a basic requirement of all living things, and the tasty food and cheap is the target of almost all students. The objective of this paper is to give an idea and the innovation of culinary business in Yogyakarta who apply the concept sociopreneur and technology as a tool to facilitate the course of this business. KedaiKampus is a startup that brings the food business operators such as food stalls, restaurants or angkringan (a traditional restaurant of Indonesia) and people who want to find the food with the best price and the best taste. The uniqueness of this business is offered weekly and monthly food packages for students in particular or for everyone who needs and will be delivered to their homes each every hour meal. KedaiKampus is also a marketspace for industrial and culinary houses, using technology based mobile application and website will allow the food industry to connect them with customers, but it also allows them to know the customer's desire for food trending in the market. The application to be developed is designed for ease of access to customers in finding their favorite foods and convenience for the culinary home to create amazing culinary innovation.

Keywords: marketplace, sociopreneur, culinary, meal

Procedia PDF Downloads 293

Authors: Faruk Inaltekin, Imran Gurakan

Abstract:

This paper will aim to discuss the importance of value-added services provided by Science and Technology Parks for entrepreneurship development in Turkey. Entrepreneurship is vital subject for all countries. It has not only fostered economic development but also promoted innovation at local and international levels. To foster high tech entrepreneurship ecosystem, Technopark (Science and Technology Park/STP) concept was initiated with the establishment of Silicon Valley in the 1950s. The success and rise of Silicon Valley led to the spread of technopark activities. Developed economies have been setting up projects to plan and build STPs since the 1960s and 1970s. To promote the establishment of STPs, necessary legislations were made by Ministry of Science, Industry, and Technology in 2001, Technology Development Zones Law (No. 4691) and it has been revised in 2016 to provide more supports. STPs’ basic aim is to provide customers high-quality office spaces with various 'value added services' such as business development, network connections, cooperation programs, investor/customers meetings and internationalization services. For this aim, STPs should help startups deal with difficulties in the early stages and to support mature companies’ export activities in the foreign market. STPs should support the production, commercialization and more significantly internationalization of technology-intensive business and foster growth of companies. Nowadays within this value-added services, internationalization is very popular subject in the world. Most of STPs design clusters or accelerator programs in order to support their companies in the foreign market penetration. If startups are not ready for international competition, STPs should help them to get ready for foreign market with training and mentoring sessions. These training and mentoring sessions should take a goal based approach to working with companies. Each company has different needs and goals. Therefore the definition of ‘success' varies for each company. For this reason, it is very important to create customized value added services to meet the needs of startups. After local supports, STPs should also be able to support their startups in foreign market. Organizing well defined international accelerator program plays an important role in this mission. Turkey is strategically placed between key markets in Europe, Russia, Central Asia and the Middle East. Its population is young and well educated. So both government agencies and the private sectors endeavor to foster and encourage entrepreneurship ecosystem with many supports. In sum, the task of technoparks with these and similar value added services is very important for developing entrepreneurship ecosystem. The priorities of all value added services are to identify the commercialization and growth obstacles faced by entrepreneurs and get rid of them with the one-to-one customized services. Also, in order to have a healthy startup ecosystem and create sustainable entrepreneurship, stakeholders (technoparks, incubators, accelerators, investors, universities, governmental organizations etc.) should fulfill their roles and/or duties and collaborate with each other. STPs play an important role as bridge for these stakeholders & entrepreneurs. STPs always should benchmark and renew services offered to how to help the start-ups to survive, develop their business and benefit from these stakeholders.

Keywords: accelerator, cluster, entrepreneurship, startup, technopark, value added services

Procedia PDF Downloads 146

Authors: Taweechai Nuntawisuttiwong, Natasha Dejdumrong

Abstract:

Geometric modeling plays an important role in the constructions and manufacturing of curve, surface and solid modeling. Their algorithms are critically important not only in the automobile, ship and aircraft manufacturing business, but are also absolutely necessary in a wide variety of modern applications, e.g., robotics, optimization, computer vision, data analytics and visualization. The calculation and display of geometric objects can be accomplished by these six techniques: Polynomial basis, Recursive, Iterative, Coefficient matrix, Polar form approach and Pyramidal algorithms. In this research, the coefficient matrix (simply called monomial form approach) will be used to model polynomial rectangular patches, i.e., Said-Ball, Wang-Ball, DP, Dejdumrong and NB1 surfaces. Some examples of the monomial forms for these surface modeling are illustrated in many aspects, e.g., construction, derivatives, model transformation, degree elevation and degress reduction.

Keywords: monomial forms, rectangular surfaces, CAGD curves, monomial matrix applications

Procedia PDF Downloads 146

Authors: Bisola Stella Sonde

Abstract:

Digital innovation has emerged as a pivotal driver of business transformation in the contemporary landscape. This case study research explores the dynamic interplay between digital innovation and the profound metamorphosis of businesses across industries. It delves into the multifaceted dimensions of digital innovation, elucidating its impact on organizational structures, customer experiences, and operational paradigms. The study investigates real-world instances of businesses harnessing digital technologies to enhance their competitiveness, agility, and sustainability. It scrutinizes the strategic adoption of digital platforms, data analytics, artificial intelligence, and emerging technologies as catalysts for transformative change. The cases encompass a diverse spectrum of industries, spanning from traditional enterprises to disruptive startups, offering insights into the universal relevance of digital innovation. Moreover, the research scrutinizes the challenges and opportunities posed by the digital era, shedding light on the intricacies of managing cultural shifts, data privacy, and cybersecurity concerns in the pursuit of innovation. It unveils the strategies that organizations employ to adapt, thrive, and lead in the era of digital disruption. In summary, this case study research underscores the imperative of embracing digital innovation as a cornerstone of business transformation. It offers a comprehensive exploration of the contemporary digital landscape, offering valuable lessons for organizations striving to navigate the ever-evolving terrain of the digital age.

Keywords: business transformation, digital innovation, emerging technologies, organizational structures

Procedia PDF Downloads 63

Authors: Jacky Hau

Abstract:

The prevalence of emerging technologies disrupts various industries as well as consumer behavior. Data collection has been in the fingertip and inherited through enabled Internet-of-things (IOT) devices. Big data analytics (BDA) becomes possible and allows real-time demand network management (DNM) through leagile supply chain. To enhance further on its resilience and predictability, governance is going to be examined to promote supply chain transparency and trust in an efficient manner. Leagility combines lean thinking and agile techniques in supply chain management. It aims at reducing costs and waste, as well as maintaining responsiveness to any volatile consumer demand by means of adjusting the decoupling point where the product flow changes from push to pull. Leagility would only be successful when collaborative planning, forecasting, and replenishment (CPFR) process or alike is in place throughout the supply chain business entities. Governance and procurement of the supply chain, however, is crucial and challenging for the execution of CPFR as every entity has to walk-the-talk generously for the sake of overall benefits of supply chain performance, not to mention the complexity of exercising the polices at both of within across various supply chain business entities on account of organizational behavior and mutual trust. Empirical survey results showed that the effective timespan on demand forecasting had been drastically shortening in the magnitude of months to weeks planning horizon, thus agility shall come first and preferably following by lean approach in a timely manner.

Keywords: governance, leagility, procure-to-pay, source-to-contract

Procedia PDF Downloads 111

Authors: Hang Wang, Xiaoming Gao

Abstract:

The rapid development of e-commerce has significantly transformed consumer behavior and urban consumption patterns worldwide. This study explores the impact of e-commerce on the development and spatial distribution of consumption centers, with a particular focus on Jinan City, China. Traditionally, urban consumption centers are defined by physical commercial spaces, such as shopping malls and markets. However, the rise of e-commerce has introduced a shift towards virtual consumption hubs, with a corresponding impact on physical retail locations. Utilizing Gaode POI (Point of Interest) data, this research aims to provide a comprehensive analysis of the spatial distribution of consumption centers in Jinan, comparing e-commerce-driven virtual consumption hubs with traditional physical consumption centers. The study methodology involves gathering and analyzing POI data, focusing on logistics distribution for e-commerce activities and mobile charging point locations to represent offline consumption behavior. A spatial clustering technique is applied to examine the concentration of commercial activities and to identify emerging trends in consumption patterns. The findings reveal a clear differentiation between e-commerce and physical consumption centers in Jinan. E-commerce activities are dispersed across a wider geographic area, correlating closely with residential zones and logistics centers, while traditional consumption hubs remain concentrated around historical and commercial areas such as Honglou and the old city center. Additionally, the research identifies an ongoing transition within Jinan’s consumption landscape, with online and offline retail coexisting, though at different spatial and functional levels. This study contributes to urban planning by providing insights into how e-commerce is reshaping consumption behaviors and spatial structures in cities like Jinan. By leveraging big data analytics, the research offers a valuable tool for urban designers and planners to adapt to the evolving demands of digital commerce and to optimize the spatial layout of city infrastructure to better serve the needs of modern consumers.

Keywords: big data, consumption centers, e-commerce, urban planning, jinan

Procedia PDF Downloads 26