IOP Conference Series: Materials Science and Engineering

Day by day the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format.

As per ISO and ASTM standards, nanoparticles are particles of sizes ranging from 1 to 100nm with one or more dimensions. The nanoparticles are generally classified into the organic, inorganic and carbon based particles in nanometric scale that has improved properties compared to larger sizes of respective materials. The nanoparticles show enhanced properties such as high reactivity, strength, surface area, sensitivity, stability, etc. because of their small size. The nanoparticles are synthesised by various methods for research and commercial uses that are classified into three main types namely physical, chemical and mechanical processes that has seen a vast improvement over time. This paper presents a review on nanoparticles, their types, properties, synthesis methods and its applications in the field of environment.

Digital marketing is an integral part of the process of digital business transformation. It incorporates new marketing techniques that are based on information and communication technologies. For this reason, its application in practice is a prerequisite for the successful development of the business in the contemporary market conditions. The object of this paper is the digital marketing and the subject is the digital marketing advantages and disadvantages. The first purpose of this paper is to systemize the various terms for digital marketing used in the specialized literature and the Internet and to show the differences between them. The second is to present the characteristics of the main advantages and disadvantages of digital marketing. Knowing them in depth, companies will be able to develop effective digital marketing strategies that have high potential to achieve company goals and at the same time are suitable to their profile. Thereby, they will be able to determine to what extent and which tools of the whole digital marketing palette are best suited to their marketing activities.

The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results.

Not all information posted on the internet is deemed 'trustworthy.' Some articles, especially those related to politics, seem to display traces of bias, whether they be for or against the Philippine administration. This research aims to determine if a news article—and by extension, a news outlet—is biased based on its sentiments and use of lexica. Data were harvested from chosen websites and news outlets provided by Alexa. These data underwent pre-processing and were scored based on their sentiments with the use of SentiWordNet. The resulting scores were then fed into the Inverse Reinforcement Model, which determined whether an article is biased or not. With the use of Inquirer, Philstar, Manila Bulletin, The Manila Times, and Journal Online news articles, the system was able to detect bias with an accuracy rating of 0.89, precision of 1, recall of 0.60 and F-Measure of 0.75.

Developing and utilization from hydrocarbon resources of northern Afghanistan is very important for ensuring the energy security and development of the least developed country in Asia. The estimation made by the USGS and Afghanistan geological survey revealed substantial oil and gas resources in the Amu Darya and Afghan-Tajik basins of northern Afghanistan that created an optimistically bright landscape for ensuring the energy availability and development of the country. This study evaluates the Amu Darya and Afghan-Tajik hydrocarbon basins and their development prospective. In 2020, field trips were arranged to Sheberghan and Sar-e-Pul oil and gas fields for collecting information, conducting interviews, and evaluating the infrastructures of oil and gas fields. The findings described that the infrastructures for producing oil and gas are old, poorly maintained, and have limited production capacity. There are serious obstacles, such as security problems, undeveloped infrastructures, and technical issues that are needed to be considered.

In this project we are designing a pressure vessel using ASME section VIII and Division 2, designing a closed container to find the required thickness of the shell, head, nozzle and leg support. Uniform thickness assigned to the entire vessel, Modelling of the pressure vessel is carried out using Pro-e 2.0; meshing is carried out using Hypermesh 6.1. Here we used 2D Quad element for the meshing, Analysis is carried out using ANSYS Software 11 for two different cases, working pressure and Maximum operating pressure, fatigue analysis is carried out, and the result is 10⁶. Finally, theoretical validation is carried out for the entire model, And the results are within the limit.

Soursop (Annona muricata) is a plant that is widely available in Indonesia. Of all parts of the soursop plant, the leaves are the most interesting parts to be studied. Soursop leaves are the most interesting part to be investigated because soursop leaves have many benefits and benefits that have been applied in the health sector, both traditional and modern. This is because soursop leaves contain many active compounds such as alkaloids, terpenoids, flavonoids, tannins, saponins, acetogenins, and others. The purpose of this review is to compare the best methods commonly used to extract active compounds from soursop leaves. The methods studied were maceration, soxhletation and microwave assisted extraction (MAE). The mechanism of the extraction processes and the percentage of yield achieved from the three methods are also reviewed. The results of the review show that MAE is the method that produces the highest yield of the three methods with a yield of 33.98%. This method also has another advantage that is a shorter extraction time.

Soil fungi have been evaluated for their ability in increasing and recovering nitrogen, phosphorus and potassium content in flooded soil and in promoting the growth of the host plant. Host plant was cultivated in a mixture of fertile forest soil (nutrient-rich soil) and simulated flooded soil (nutrient-poor soil) in an optimized soil condition for two weeks. The soil sample was harvested every day until two weeks of planting and was tested for nitrogen, phosphorus and potassium concentration. Soil fungi were isolated by using dilution plating technique and was identified by Biolog's Microbial Systems. The concentration of nitrogen, phosphorus, and potassium was found to be increasing after two weeks by two to three times approximately from the initial concentration recorded. Two fungi species were identified with probability more than 90% namely Aspergillus aculeatus and Paecilomyces lilacinus. Both identified fungi were found to be beneficial in enhancing plant growth and increasing the availability of nutrient content in the soil and thus recovering the nutrient content in the flooded soil.

Development of financial management theory developed rapidly; forming branches roots. Start with Value of the firm theory, capital structure theory up to investment theory. Investment theory; behavioural finance is relatively new field that combine behavioural, psychological, economics and finance. This paper aims to develop conceptual Framework of factors that Influence Financial Literacy. Research in factors that influence financial literacy gives new development of financial theory through perception view. This research use qualitative study with grounded theory model of financial literacy. Moreover, this research gives implication in comprehensive framework that can be used in developing future research.

With the rapid rise and recent success of artificial intelligence, there has been growing interest in the significance of embodiment, the formal limits of AI, and the nature of cognition. Additionally, the increasing computational complexity required for training AI models has motivated the research community to explore alternative methods of computation. One promising direction is leveraging the physical properties of agents' bodies to naturally simplify problems and reduce computational demands. These challenges call for a multidisciplinary approach, incorporating insights not only from computer science and statistics but also from philosophy, biology, mechanical and materials engineering.

The International Conference on Embodied Intelligence, now in its third year, has firmly established itself as a leading venue for bringing together researchers from diverse disciplines such as robotics, material science, sociology, philosophy, computer science, and beyond. This conference provides a unique, interdisciplinary platform where experts can come together to explore the rich, multifaceted concept of embodied intelligence. Through these discussions, the conference continues to shape research agendas and guide new directions for this rapidly evolving field. Our invited plenary speakers have been instrumental in steering the conversation, addressing some of the most critical challenges at the heart of embodied intelligence. Their presentations, along with dynamic panel discussions, have sparked deeper reflections and collaborative efforts aimed at finding practical solutions.

In this proceedings volume, contributors from across the conference including short talks, plenaries, and breakout sessions, share their insights on the pressing issues and challenges discussed at the event. Their articles also examine potential solutions, emerging trends, and future perspectives, ensuring that the dialogue surrounding embodied intelligence continues to grow and expand beyond the confines of the conference.

The book opens by offering a range of perspectives and outlooks on Embodied Intelligence, followed by chapters showcasing examples of soft robotics and Morphological Computing systems. The final chapters focus on Artificial Life and Intelligence including concepts of cognition and learning, as well as the philosophical and conceptual issues that arise in the study of Embodied Intelligence.

We extend our heartfelt thanks to all seminar participants, authors, and reviewers for their outstanding contributions to this volume, which would not have been possible without their collective expertise and dedication.

Arsen Abdulali

Josie Hughes

Fumiya Iida

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Open Review

• Conference submission management system: Morressier

• Number of submissions received: 17

• Number of submissions sent for review: 17

• Number of submissions accepted: 17

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 100

• Average number of reviews per paper: 3.0588235294117645

• Total number of reviewers involved: 20

• Contact person for queries:

Name: Arsen Abdulali

Email: aa2335@cam.ac.uk

Affiliation: University of Cambridge - Engineering

This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.

Algorithms that employ habitual processing may be characterized as context-contingent input-output pairings. They learn to relate recurring patterns with swift and effective responses, relying on minimal computational resources. While habits have been extensively researched in the fields of psychology, cognition, and neuroscience, they have yet to be systematically investigated in computer science. Data volumes are expanding at an exponential rate, and there is often a requirement for this data to be processed in real-time. Against this backdrop, principles of habitual cognition present a promising alternative to resource-intensive planning algorithms. We propose that future research focus on algorithmic and neuronal models of the neurocognitive foundations of habits to develop new perspectives on artificial intelligence and robotics.

This article explores how, in the 1982 film Blade Runner (directed by Ridley Scott), some preeminent creative minds predicted a future which increasingly reflects the realities of our age. The research outcome is multidisciplinary in perspective, drawn from interviews with the filmmakers as well as with thought leaders in Artificial Intelligence, Classics, Engineering, Ethics, Philosophy, Robotics, and Theology, and is, in part, influenced by the seminal work of Alessandro Portelli. The author has also structured the article by applying some of Jonathan S. Feinstein's models of creative engagement in exploring the intricacies of creative and innovative work in the arts and sciences, as developed in his book The Nature of Creative Development (2006). The article contributes to the debate about the numerous contentions within the text which it seeks to clarify, as well as to the debate about the nature of creative work and the extent to which the visionaries involved could benefit from a cross-pollination of ideas between disciplines.