Recent Submissions

  • Clustering potential metaverse users with the use of a value-based framework: Exploiting perceptions and attitudes on the use and adoption of metaverse for bold propositions

    Rsha Mirza; Lytras, Miltiadis; Ohoud Alzamzami; Lama Al Khuzayem; Hajar Alharbi; Sultanah Alshammari; Alaa Bafail; Arwa Basbrain; Eaman Alharbi; Nada Bajnaid; et al. (Pergamon, 2024-03-01)
    Metaverse is a new emerging platform that enables users to interact with each other and engage in many activities inside a collective virtual shared space. The metaverse is rapidly evolving, and it is essential to understand the perception and attitude of its potential users. Therefore, this research aims to study the users of the metaverse and obtain their opinions on some of the key variables of the metaverse. These variables include the core metaverse concept, readiness, ease of use, intention to belong to metaverse, intention to adopt, and value realization. We designed an online questionnaire that aims to measure these variables. Then, a deep analysis of the collected data was conducted using various exploratory descriptive statistics on all participants. Subsequently, we performed further descriptive and correlation analysis on the participants from Saudi Arabia, which resulted in clustering participants into three groups. The result of this work can help in understanding the current and potential users of the metaverse, especially the users from Saudi Arabia. This study will consequently help in developing the metaverse space, enhancing its features, and providing users with the best experiences. Our research contributes to the theory of the metaverse by justifying a bold value-based framework for metaverse adoption. This study also introduces three clusters, Skeptical, Unaware, and Optimists, of potential users of metaverse platforms, providing a clear description of each group.
  • An improved clustering method using particle swarm optimization algorithm and mitochondrial fusion model (PSO-MFM)

    Elkafrawy, Passent; Nasef, Mohamed; Hashim, Amal; External Collaboration; Virtual Reality Lab; 0; 0; Computer Science; 1; Nasef, Mohamed (IOS Press BV, 2024-02-16)
    Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity.
  • Advancing precision medicine in medical education: Integrated, precise and data-driven smart solutions

    Paraskevi Papadopoulou; Lytras, Miltiadis; External Collaboration; NA; NA; NA; Computer Science; NA; Paraskevi Papadopoulou (2023-12-02)
    Advances in “Precision Medicine” initiative, also known as “Personalized Medicine” is an emerging approach for disease treatment and prevention that has already led to innovative discoveries and has created “smart” applications and solutions tailored to a person's or a group of individuals' genetic profile, lifestyle, and environment interaction. Already many physicians as part of patient care routinely prescribe various molecular/genetic and other tests enabling them to select personalized treatments that improve the chances of survival and reduce exposure to adverse effects. This initiative should provide to medical and healthcare professionals with adequate resources and readily available solutions so that the target to specific treatments and care of the illnesses is achieved while at the same time protecting the privacy and safety of the individual is secured as well as the Electronic Health Records (EHRs) and whatever additional data is necessary within the context of Precision Medicine. This study, through a literature review mostly, and some case study analysis, examine whether personalized medicine is delivered to the patient in an “accurate” and “precise” way, as expected. This requires that Health and Human Services and other stakeholders and agencies collaborate to solicit the right input from patients while at the same time can identify and address any educational, practical, legal, and technical issues and providing smart solutions. The lack of proper medical education and advanced infrastructure are still major barriers to the adoption of Precision Medicine, therefore, the role of medical training in Precision Medicine is also examined and analyzed. Specific examples are discussed in an integrated, precise, and data-driven manner to provide “smart” solutions.
  • Translating a value-based framework for resilient e-learning impact in post COVID-19 times: Research-based Evidence from Higher Education in Kuwait

    Afnan Alkhaldi; Sawsan Malik; Rashed Alhaimer; Abdullah Alshaheen; Lytras, Miltiadis; External Collaboration; NA; NA; NA; Computer Science; et al. (Elsevier, 2024-01-30)
    The covid-19 pandemic has changed people’s daily lives and ehaviors all across the world and has impacted practically every element of human existence. The introduction of remote education systems and the move toward online learning have had some of the most ignificant effects. The on-site operations of educational institutions, such as schools, colleges, and universities, have had to be suspended in order to stop the virus’ spread. In order to effectively disseminate instructional material and guarantee the unbroken progression of students’ academic endeavors, educators have been forced to look for novel approaches. The study used the Value-Based Adoption Model (VAM) as a conceptual framework to look into the factors that affected Kuwait’s e-learning outcomes in the midst of the covid-19 pandemic. 382 students at Kuwaiti universities and colleges were the source of quantitative data collection. The findings revealed that peer interaction emerged as the most influential factor in shaping utcomes within the educational context of Kuwait, while instructors and course design factors were not significant. Using the VAM, this study investigated the impact of several factors on students’ e-learning results during times of crisis. The research expands the existing knowledge base in the field on this subject and suggests developing a well-organized online learning crisis approach. The main contribution of this work is summarized on (i) An integrated framework for the quality of the e-learning experience in universities in post-covid-19 times and (ii) A resilient higher education institutional learning strategy model in post-covid-19 times. The findings of this paper can be generalizable to other Gulf Corporation Council (GCC) countries such as Kingdom of Saudi Arabia, Qatar, United Arab Emirates (UAE), Bahrain and Oman. This is due to the shared cultural traditions and values, along with similar educational systems among these nations.
  • Guest editorial: Gender, entrepreneurship and the digital divide: a global perspective

    Anna Visvizi; Lytras, Miltiadis; Chuman, Merwat; Sarirete, Akila; Kozłowski, Krzysztof; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; et al. (Emerald Publishing Limited, 2023-10-11)
    The concepts and terms defining the thrust of this special issue, i.e. gender, entrepreneurship (Berger et al., 2021), the digital divide (Bowen and Morris, 2019; Millan et al., 2021; Satalkina et al., 2021), Global South (Simaan, 2020) and Global North, are very well established in the literature. Nevertheless, relatively little has been written about (1) the gendered dimension of the digital divide, (2) the digital divide and the gendered dimension of entrepreneurship (Elliott et al., 2021); and finally, (3) the specificity of these topics as they are in the Global South and Global North's peripheries (Ojediran and Anderson, 2020; Wood et al., 2021; Althalathini et al., 2020). Even if research on each of these individual domains exists, relatively little research on the intersection of these three areas exists (but cf. Visvizi et al., 2023, and earlier Kasusse, 2005; Alden, 2003). Notably, given the pace and the pervasive impact of digital transformation globally, and their diverse political, social and economic manifestations, it is necessary that the mechanisms underlying these interconnected issues and developments are identified and explored. This special issue sought to encourage this kind of conversation, always in context of the United Nation's (UN) Sustainable Development Goals (SDGs). Specifically, the key objective of this special issue was to examine, (1) how the onset of increasingly sophisticated information and communication technology (ICT) influences gender and entrepreneurship in the Global South and in the peripheries of the Global North; (2) what types of interpretive lens and explanatory potential are offered by the existing literature on the subject and (3) whether best practice-sharing and specific business and policy strategies might be helpful in alleviating negative implications of the global digital transformation.
  • Interpretable Detection of Malicious Behavior in Windows Portable Executables using Multi-Head 2D Transformers

    Khan, Sohail; Nauman, Mohammad; Department Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; Khan, Sohail (2023-11-17)
    Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on systems. One of the major challenges in tackling this problem is the complexity of malware analysis, which requires expertise from human analysts. Recent developments in machine learning have led to the creation of deep models for malware detection. However, these models often lack transparency, making it difficult to understand the reasoning behind the model’s decisions, otherwise known as the black-box problem. To address these limitations, this paper presents a novel model for malware detection, utilizing vision transformers to analyze the opcode sequences of more than 350,000 Windows portable executable malware samples from real-world datasets. The model achieved a high accuracy of 0.9864, not only surpassing previous results but also providing valuable insights into the reasoning behind the classification. Our model is able to pinpoint specific instructions that lead to malicious behavior in malware samples, aiding human experts in their analysis and driving further advancements in the field. We report our findings and show how causality can be established between malicious code and actual classification by a deep learning model thus opening up this blackbox problem for deeper analysis.
  • An Efficient Encryption and Compression of Sensed IoTMedical Images Using Auto-Encoder.

    ElKafrawy, Passent; elsayed, Ayman; Torkey, Hanaa; Ahmed, Abdelmoty M.; Aboghazalah, Maie; External Collaboration; Virtual Reality Lab; 0; 0; Computer Science; et al. (EBSCO, 31 August )
    Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption ofmedical images is very important to secure patient information. Encrypting these images consumes a lot of time onedge computing; therefore, theuse of anauto-encoder for compressionbefore encodingwill solve such a problem. In this paper, we use an auto-encoder to compress amedical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted. Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder, which is utilized to compress and encrypt the images, and the other assesses the classification accuracy of the image after decryption and decoding. Different hyperparameters of the encoder were tested, followed by the classification of the image to verify that no critical information was lost, to test the encryption and encoding resolution. In this approach, sixteen hyperparameter permutations are utilized, but this research discusses three main cases in detail. The first case shows that the combination of Mean Square Logarithmic Error (MSLE), ADAgrad, two layers for the auto-encoder, and ReLU had the best auto-encoder results with a Mean Absolute Error (MAE) = 0.221 after 50 epochs and 75% classification with the best result for the classification algorithm. The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error (MSE), RMSprop, three layers for the auto-encoder, and ReLU, which had the best classification accuracy of 65%, the auto-encoder gives MAE = 0.31 after 50 epochs. The third case is the worst, which is the combination of the hinge, RMSprop, three layers for the auto-encoder, and ReLU, providing accuracy of 20% and MAE = 0.485.
  • A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

    Mohammed Balfaqih; Zain Balfagih; Miltiadis D Lytras; Khaled Mofawiz Alfawaz; Abdulrahman A Alshdadi; Eesa Alsolami; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; et al. (MDPI, 2023-09-20)
    The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
  • Risk prediction model for cannabis use with artificial intelligence approach

    Hakkarainen, P; Karjalainen, K.; Subasi, A; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; Unlu, A. (Taylor and Francis, 01/08/2023)
    Background Identifying the most important predictors of substance use is crucial for developing effective prevention policies. Traditional statistical methods have some limitations in this regard. To address these limitations, the researchers utilized artificial intelligence (AI) methods to identify the top 10 most important predictors of cannabis use in Finland. Objective The objective of this study was to apply AI techniques to identify the key predictors of cannabis use in Finland. Specifically, the researchers aimed to determine the top 10 most important features related to cannabis use from a dataset consisting of 3229 observations and 313 questionnaire items, with 48 selected for preprocessing. Methods The researchers employed the recursive feature elimination (RFE) method as part of their AI analysis. This technique was used on 60 processed variables, following the application of missing data imputation, resampling, and scaling techniques. The RFE method allowed the researchers to narrow down the 60 variables to the top 10 most important features associated with cannabis use. Results The AI models developed using the selected features were able to predict cannabis use with a remarkable accuracy of 96% for the previous 12 months. The results of the study revealed that the social settings of individuals played the most significant role in predicting cannabis use in the context of Finland. Conclusions In conclusion, this study demonstrated the effectiveness of AI-based approaches in identifying the most critical predictors of cannabis use in Finland. The research highlighted that social settings had the highest impact on cannabis use in this setting. Moreover, the study showcased the potential of AI methods not only for identifying key risk indicators among various factors but also for optimizing the utilization of limited public resources when devising prevention strategies. These findings can be valuable for shaping targeted and efficient prevention policies to address cannabis use in Finland.
  • Promoting accuracy in low-magnification histopathology grading: With augmentation and multi-dilation model.

    Subasi, Abdulhamit; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; Gan, Zonghan (Elsevier, September )
    Advances in artificial intelligence have facilitated the automated grading of histopathology slides. Yet, the magnification of whole slide scanners (WSS) has restrained the accuracy of patch-based grading. In this work, we found that augmentation can significantly promote grading performance under this issue, even when the data volume is large (>140 K). With augmentation and a multi-dilation model, the CovXNet, we yielded a Balanced Accuracy of 92.13%, which is the current highest for the Breast Histopathology Dataset (40X magnification) also the first time both sensitivity and specificity >90%. However, in this focused grading task, augmentation only improves models with high invariance (the CovXNet and BCA-CNN). Pre-trained ResNet has lower invariance in this task, but fine-tuning can significantly improve both accuracy and invariance. For the CropNet attention model, adapting with max pooling but not augmentation offers promotions. Additionally, this work also found two types of common errors in high-starred codes, when using random.shuffle for data-label composited array, or the integrated shuffle function of ImageDataGenerator, which fake a higher accuracy by masking class 0 as class 1. Using Sklearn.shuffle instead is safer. All codes are available on our GitHub.
  • A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

    Balfaqih, Mohammed; Balfagih, Zain; Lytras, Miltiadis; Alfawaz, Khaled; Alshdadi, Abdulrahman; Alsolami, Eesa; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; et al. (MDPI, 2023-09-20)
    The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
  • The influence of AmeriCorps members on ecosystem management, Journal of Cleaner Production

    Zhuhadar, Lily Popova; McCreary, Allie; Miltiadis, Lytras; Maria, Wells; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; et al. (Elsevier, 2023-09-25)
    Millions of acres of public lands across the United States (U.S.) face imminent threats from invasive species and wildfires, jeopardizing ecosystem health, wildlife habitats, recreational resources, and community safety. The responsible agencies, including the U.S. Forest Service, National Park Service, Bureau of Land Management, and state park agencies, are limited in their capacity to effectively manage these lands. Consequently, AmeriCorps members play a crucial role in curbing invasive species proliferation and mitigating wildfire fuel loads, safeguarding communities, and habitats. Here, the results of a national evaluation to assess the impact of AmeriCorps members on invasive species and wildfire fuel loads is presented. This study is a comprehensive analysis of AmeriCorps members' effectiveness in achieving ecosystem management objectives, revealing positive responses in native species cover and notable improvements in treated plots. AmeriCorps units that yielded significant results operated under specific temperature conditions and adhered to distinct pre- and post-intervention data collection intervals. Despite occasional challenges, the overall efficacy of AmeriCorps' efforts in eradicating targeted invasive species and promoting positive native species responses was confirmed. While linear regression models indicated successful forest fuel mitigation in AmeriCorps programs, the Difference-in-Difference models revealed less conspicuous outcomes, suggesting limited modifications in parameters such as the height of the lowest living branch, canopy cover, and litter depth. By analyzing data from multiple AmeriCorps programs in diverse geographic regions, this study contributes valuable insights into the effectiveness of AmeriCorps programs in ecological conservation. It represents one of the first multi-state and comprehensive examinations of AmeriCorps members' effectiveness in invasive species management and wildfire fuel mitigation in the USA, underscoring its significance in the field of ecosystem management.
  • The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions

    Zhuhadar, Lily Popova; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; Zhuhadar, Lily Popova (MDPI, 2023-09-04)
    Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
  • A Blockchain Based Framework for Efficient Water Management and Leakage Detection in Urban Areas

    Mohammad, Naqash; Toqeer, Syed; Saad, Alqahtani; Muhammad, Siddiqui; Ali, Alzahrani; Mohammad, Nauman; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; et al. (MDPI, 2023-09-22)
    Sustainable urban water management is essential to handle water scarcity, leakage, and inefficient distribution. This paper covers water management in urban areas, including an intro- duction, an overview of water management practices, the characteristics and functioning of water distribution systems, monitoring and control systems for efficient distribution, smart systems for optimization, strategies for water conservation and waste management, per capita water demand analysis, and desalination plant overviews. The article proposes a blockchain-based water manage- ment architecture with IoT sensors for accurate reporting. The framework uses blockchain technology to authenticate and share real-time data between sensors and the water distribution dashboard. It also has a modular API for water leakage detection and flow control to decrease water waste and enhance distribution. The suggested approach might enhance water management; however, its execution is complex. Maintaining the framework’s efficacy is advised. The research provides insights into water management and proposes a technology solution employing blockchain and IoT sensors for trustworthy data reporting and effective water distribution to promote sustainable urban water management.
  • Government and the global digital transformation: the other side of the mirror

    Visvizi, Anna; No Collaboration; NA; 0; 0; Entrepreneurship; 0; Visvizi, Anna (2023-10-30)
    Amid the global digital transformation, the seemingly simple question of the government delivering on peace, justice and strong institutions turns into a conundrum. Several factors weigh in on what the government can do, envisages to do and dares to do today. State power, raison d’étre, the nature of the global order, including global governance, are just a few of the issues that need to be factored in the analysis. Clearly, questions of ideology, including the degree of government intervention in the economy, the very definition of market economy and even of the scope of (economic) freedom are equally important in the conversation about the government today. People, and so personalities, do matter too, in that they influence the way things are done, expressed and communicated, regardless of the institutional, i.e. frequently rigid, structural confines.
  • A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

    Mohammed Balfaqih; balfagih, zain; Lytras, Miltiadis; Khaled Mofawiz Alfawaz; Abdulrahman A Alshdadi; Eesa Alsolami; External Collaboration; NA; NA; NA; et al. (MDPI, 2023-09-20)
    The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
  • The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions

    Lily Popova Zhuhadar; Lytras, Miltiadis; External Collaboration; NA; NA; NA; Computer Science; NA; Lily Popova Zhuhadar (MDPI, 2023-09-08)
    Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
  • Study of image sensors for enhanced face recognition at a distance in the Smart City context

    Llaurado, Jose M.; Pujol, Francisco A.; Tomas, David; Visvizi, Anna; Pujol, Mar; External Collaboration; NA; 0; 0; Computer Science; et al. (Springer, 2023-09-07)
    Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
  • Think human, act digital: activating data-driven orientation in innovative start-ups

    Visvizi, Anna; Troisi, Orlando; Grimaldi, Mara; Loia, Francesca; External Collaboration; NA; 0; 0; Entrepreneurship; 0; et al. (Emerald Publishing, 2022-12-19)
    The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation. Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined. The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.
  • Government regulation and organizational effectiveness in the health-care supply chain

    Hussain, M.; Ahmad, S.Z.; Visvizi, Anna; External Collaboration; NA; 0; 0; Entrepreneurship; 0; Hussain, M. (Emerald Publishing, 2022-12-10)
    In the context of the debate on ensuring health-care efficiency, this study aims to identify and prioritize factors and subfactors that influence organizational effectiveness (OE) in the health-care supply chain. For the purpose of this qualitative study, triangulation was applied to identify, explore and systematically analyze the OE-related practices used by diverse stakeholders along the health-care supply chain. Sixty-two OE practices were thus identified. Subsequently, these were grouped in six different nodes before the analytical hierarchical process (AHP) was used to identify the weightings of specific practices (and related factors) and their impact on OE. The findings suggest that external factors associated with government regulation, including government directives and branding, are the most critical factors that influence OE-related practices, while cost-related factors are the least important.

View more