Younes Akbari, Faseela Abdullakutty, Somaya Al Maadeed
et al.
Abstract Accurate breast cancer survival prediction using multi-modal data is vital for enhancing clinical decisions. This study evaluates deep learning based fusion strategies, early, intermediate, late, and a hybrid approach, to integrate histopathology images and genomic data for one year survival prediction. We developed a robust evaluation framework, employing tailored deep learning architectures and metrics including accuracy, precision, recall, F1 score, and AUC. Model performance was validated using Kaplan–Meier curves and log-rank tests, with SHAP-based feature importance analysis enhancing interpretability. Results highlight the strengths and limitations of each fusion strategy, offering insights into optimal multi-modal learning approaches for breast cancer prognosis. Our findings underscore the importance of selecting task specific fusion methods, providing a reproducible, interpretable framework to advance survival prediction. All code and configurations are publicly available.
Electronic computers. Computer science, Information technology
Majid Aljalal, Saeed A. Aldosari, Marta Molinas
et al.
Abstract Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.
Introduction
Recent advances in artificial intelligence (AI) have recaptured and revised the essential roles of death in life and mind. However, their prospects and risks require further study. Because of the development of digital technologies (for example, AI-based chatbots), the process of bereavement may have become complex, immersive, and even addictive. Furthermore, AI-enabled generation of medical notes can ease the administrative burden for healthcare professionals; however, the clinical application of generative AI remains largely speculative.
Objectives
This study aimed to illuminate the emerging concept and experience of death, bereavement, and addiction associated with cybernetics, thereby expanding their cognitive and ethical aspects.
Methods
In this preliminary review, we performed a literature search to identify the current state-of-the-art literature on AI and Internet addiction. We also inspected the possible adaptations to pursue mental well-being with the modified death concept. We mainly searched the PubMed and Web of Science databases using relevant keywords. All retrieved studies were assessed for eligibility to reduce the selection bias.
Results
Current cybernetics have meaningfully recontextualized death that allows interaction with deceased individuals (for example, scholars and artists) to establish their virtual, besides biological, existence using AI-based chatbots. Furthermore, AI consistently provides evidence-based answers to public health inquiries; nevertheless, it may offer unsuitable advice rather than referrals that can sometimes facilitate suicide or harm (instead of help) people in grief, thus requiring more fine-tuned governance. Accordingly, the maladaptive use of existing AI-related communication (such as metaverse characters) can increase Internet addiction prevalence and further complicate autonomy and self-motivation. In addition, excessive internet access is frequently associated with reduced self-control, cognitive flexibility, and exaggerated automatic processing.
Conclusions
We are challenged to acknowledge the tradeoffs of AI and consider ways to compromise by employing flexible perspectives. The emerging concept of death affects or improves the conventional one. The potential advantages and pitfalls of AI-related technology must be carefully weighed against the profound effects they may have on people’s identities, relationships, and mental health. These issues require continued monitoring and assessment in light of the AI/cybernetic-related studies. We hope these results will inspire further research into the appropriate use of AI and palliative care, including suicide prevention, euthanasia, and grief management.
Disclosure of Interest
None Declared
Abstract Quasi-phase matching (QPM) is a technique extensively utilized in nonlinear optics for enhancing the efficiency and stability of frequency conversion processes. However, the conventional QPM relies on periodically poled ferroelectric crystals, which are limited in availability. The 3R phase of molybdenum disulfide (3R-MoS2), a transition metal dichalcogenide (TMDc) with the broken inversion symmetry, stands out as a promising candidate for QPM, enabling efficient nonlinear process. Here, we experimentally demonstrate the QPM at nanoscale, utilizing van der Waals stacking of 3R-MoS2 layers with specific orientation to realize second harmonic generation (SHG) enhancement beyond the non QPM limit. We have also demonstrated enhanced spontaneous parametric down-conversion (SPDC) via QPM of 3R-MoS2 homo-structure, enabling more efficient generation of entangled photon pairs. The tunable capacity of 3R-MoS2 van der Waals stacking provides a platform for tuning phase-matching condition. This technique opens interesting possibilities for potential applications in nonlinear process and quantum technology.
The purpose of the present study was to assess acceptance of Artificial Intelligence Application in the Post-covid Era and its impact of faculty members’ occupational well-being and teaching self efficacy using The UTAUT 2 Model. This study used a quantitative, non-experimental survey design to answer the research questions and study the relationships between the independent variables of performance expectancy, effort expectancy, social faculty members’ occupational well-being and teaching self efficacy. Faculty members from Umm AL-Qura University were targeted. An online questionnaire was used to collect data via Facebook and WhatsApp groups. I received a total of 350 questionnaire responses. They were 200 males(57.1%), and 150 females(42.9%). In confirmation of the research results, there is a significant positive relationship (p < .001) between occupational well-being (OWB)and teaching self efficacy(TSE) and performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), and habit (HB), indicating that faculty members are influenced by the constructs established in the UTAUT2 model in the adoption of AI.
Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project – funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role.
Charalampos Orfanidis, Atis Elsts, Paul Pop
et al.
Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard. It has proven to be one of the most reliable options when it comes to industrial applications. TSCH offers a degree of high flexibility and can be tailored to the requirements of specific applications. Several performance aspects of TSCH have been investigated so far, such as the energy consumption, reliability, scalability and many more. However, mobility in TSCH networks remains an aspect that has not been thoroughly explored. In this paper, we examine how TSCH performs under mobility situations. We define two mobile scenarios: one where autonomous agriculture vehicles move on a predefined trail, and a warehouse logistics scenario, where autonomous robots/vehicles and workers move randomly. We examine how different TSCH scheduling approaches perform on these mobility patterns and when a different number of nodes are operating. The results show that the current TSCH scheduling approaches are not able to handle mobile scenarios efficiently. Moreover, the results provide insights on how TSCH scheduling can be improved for mobile applications.
Background: The adoption of cloud services can enable enterprises to realise improved cost structures, agility and productivity, yet the rate of adoption has been measured. Despite the benefits of cloud computing and the fact that the overall adoption of public cloud services is gaining momentum, South African large enterprises are cautious in adopting the services of cloud service providers because of perceived challenges of cloud adoption.
Objectives: The objective of this study was to examine how do South African large enterprises assess and advance their cloud readiness and maturity such that cloud service practices contribute positively to business efficiency and agility whilst mitigating against the perceived risks of cloud computing.
Method: This research employed a qualitative approach using in-depth interviews. Sixteen South African large enterprise cases were studied by interacting with respondents associated with cloud decision-making. Data were collected from specific cases, utilising non-probability sampling.
Results: Reinvention of the organisation can be enabled through the advanced, integrated cloud and analytic features available through the global public cloud providers such as artificial intelligence and machine learning. A cloud maturity framework and a cloud capability maturity model to optimise and advance cloud maturity status are presented.
Conclusion: This article guides information technology (IT) managers to achieve an optimal cloud maturity status level using a proposed cloud capability maturity model. The cloud framework developed in this study will assist IT managers and decision-makers to use evidence-based management principles to determine their maturity of cloud adoption.
Management information systems, Information theory
Teresa Cristóbal, Gabino Padrón, Alexis Quesada-Arencibia
et al.
In road-based mass transit systems, the travel time is a key factor affecting quality of service. For this reason, to know the behavior of this time is a relevant challenge. Clustering methods are interesting tools for knowledge modeling because these are unsupervised techniques, allowing hidden behavior patterns in large data sets to be found. In this contribution, a study on the utility of different clustering techniques to obtain behavior pattern of travel time is presented. The study analyzed three clustering techniques: K-medoid, Diana, and Hclust, studying how two key factors of these techniques (distance metric and clusters number) affect the results obtained. The study was conducted using transport activity data provided by a public transport operator.
Violetta P. Sherendak, Ivan A. Bratchenko, Oleg O. Myakinin
et al.
In the paper, we present test results of methods for the noninvasive diagnosis of skin neoplasms, based on the hyperspectral registration of images by using a camera with an acousto-optic tunable filter. For the identification of oncological pathologies, an integral spectral index has been proposed for a set of concentric regions around the source of neoplasm growth for the tissue sample under study. As well as taking account of changes in the spectral properties of the tissue, the introduced index indirectly takes into account classical ABCD dermatoscopic features: asymmetry, border irregularity, color diversity, and the tumor diameter. Results of training set separating are presented and the applicability of the proposed approaches to the clinical practice is shown.
E-Government basically comprises the use of electronic communications technologies such as the internet, in enhancing and advancing the citizens access to public services. In most developing countries including Burundi, citizens are facing many difficulties for accessing public services. One of the identified problems is the poor quality of service in managing citizens’ complaints. This study proposes an SMS and web based e-Government Model as a solution. In this study, a case study of a complaint management system at District of Gihosha has been used as a reference to prove that SMS and Web based e-Government Model can enhances the access of public services. The objective of this study is the development of an SMS and web-based system that can enhances the process and the management of citizens’ complaints at District of Gihosha. The system has been developed using PHP as front end, Apache as web server, MySQL as Database and Gammu as SMS gateway. The obtained results after testing the system shows that all the functionalities of the developed system worked properly. Thus, the SMS and web based complaint management system developed is considered to be effective.
Knowledge can get lost when workers leave the company, or it may be missed when new challenges emerge. Specific knowledge may be important for the value-added chain of an organization, and its inaccessibility could be a problem. The work on this paper seeks to juxtapose this problem with the concept of intangible knowledge. This concept is developed as an observation model for particular situations within organisations, in which specific, useful, knowledge is no longer available and is being missed. This paper considers a potentially useful way to deal with absence of such knowledge by using the social science approach. In addition to social systems theory, the communication and cultural science view was selected here to propose a new understanding of the function of knowledge as a communicational or cultural parameter within structures and meanings of a social system. This should facilitate a better perception of the actions and dynamics inside organizations regarding knowledge or the lack thereof.
The Standish Group started in 1985 in the business of IT markets forecasts and predictions using Artificial Intelligence and cased-based reasoning technology. In 1994 we turned to predicting project outcomes, improving software development, and building a world-class database. Standish's cumulative research encompasses 22 years of data on why projects succeed or fail, representing more than 50,000 active completed IT projects. In this paper we clarify how we got here, where we are, and how academia next to practitioners can be part of the next stage of the CHAOS journey. The vehicle which drives our journey is the CHAOS University System.
The paper presents a new method of hardware virtualization detection on server computers and workstations, which is protected from interdiction of modern software approaches.