Aleksandar Karadimce

Macedonia University St. Paul the Apostle - Ohrid

Webinar

Commented on Research and development activities for a wearable brain-computer interface based on spontaneous brain activity measurement
It is a great book by authors that provides a comprehensive review of the primary EEG-based Brain-Computer Interface (BCI) paradigms, as well as practical solutions for their design, prototyping, and testing. Readers will learn about active, reactive, and passive BCI paradigms, focusing on the operation for generating solutions that fulfill the demand for customisation. https://www.routledge.com/Wearable-Brain-Computer-Interfaces-Prototyping-EEG-Based-Instruments-for/Arpaia-Esposito-Gargiulo-Moccaldi/p/book/9781032200859

Article

Commented on Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier
In recent years, many researchers have been using the concept of machine learning to predict DM disease. Some of the commonly used algorithms include logistic regression (LR), XGBoost (XGB), gradient boosting (GB), decision trees (DTs), ExtraTrees, random forest (RF), and light gradient boosting machines (LGBM). Each classifier has its advantages over the other classifiers. However, the classifier that gives the highest accuracy is determined in implementation.

Article

Commented on Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
Authors have used the ML technique based on risk stratification, which is developed, optimized and evaluated. Features are optimized using six feature selection techniques. Then, the PIMA Indian diabetes dataset (PIDD) is used. The ten different classifiers are used. Both RF selection and RF classification techniques yield an accuracy of 92.26%.

Journal

Commented on MOBILE NETWORKS & APPLICATIONS
EAI MLICOM 2024 - 9th EAI International Conference on Machine Learning and Intelligent Communications Conference on November 17-18, 2024 Conference web page https://mlicom.eai-conferences.org/2024/ All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library. Authors of selected papers will be invited to submit an extended version to: Mobile Networks and Applications (MONET) Journal [IF: 3.077 (2021)]

Hub

Commented on Conferences 2023-2024
EAI MLICOM 2024 - 9th EAI International Conference on Machine Learning and Intelligent Communications Full Paper Submission deadline 30 August 2024 Notification deadline 30 September 2024 Camera-ready deadline 20 October 202 Conference on November 17-18, 2024 All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library. Proceedings will be submitted for inclusion in leading indexing services, such as Web of Science, Compendex, Scopus, DBLP, EU Digital Library, Google Scholar, IO-Port, MathSciNet, Inspec, and Zentralblatt MATH. Submit paper https://mlicom.eai-conferences.org/2024/

Hub

Commented on Impact of AI on Cybersecurity
Webinar, “Generative AI Unleashed: Reshaping the Future of Work” on 12 February, featuring a talk by Ben Ellencweig, Senior Partner at McKinsey & Company and Leader of QuantumBlack, AI by McKinsey, to explore the impact of Generative AI on work and industries. This session will delve into how this groundbreaking technology revolutionises business processes, enhances creativity, and drives operational efficiency across sectors while transforming financial services and decision-making. Discover practical applications, success stories, and future trends of Generative AI during this event, which is ideal for anyone eager to understand the role of AI in shaping the future of our workplaces, industries and financial systems. The presentation follows a Q&A session moderated by Dr. Sarah Hammer of The Wharton School. This live event includes a 15-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community. https://aiforgood.itu.int/event/generative-ai-unleashed-reshaping-the-future-of-work/

Article

Commented on Accurate and rapid screening model for potential diabetes mellitus
The authors evaluated the classifiers using nine clinical features that were easily collected and non-invasive. The J48 classifier performed the best, demonstrating that decision tree analyses can be used to promptly and accurately screen for diabetes in clinical practice. This work is critical in areas with high epidemic risk and low socioeconomic levels. The tree structure reveals the most significant risk factors and recommends that diabetes preventive programs be implemented through focused community interventions.

Article

Commented on A review of data mining applications in crime
This review is unusual in that it includes over 100 applications and is the most current and comprehensive review of Data Mining applications in crime to date. We find significant evidence based on the number of applications to conclude that classification techniques are the most used type of Data Mining in crime. The authors hope that this review study will assist academics throughout the world in quickly identifying existing studies, allowing them to develop more complicated and improved strategies for mining crime data in the future.

Article

Commented on Artificial Intelligence: The Future for Diabetes Care
An overview article on using AI for the management of diabetes AI has enhanced both patient transfer inside hospitals and patient flow to hospitals. Online diabetes communities and support groups allow patients to connect and learn from one another's experiences. Patients and caregivers find this collaborative approach to learning more about the disease to be interesting, and it has a good effect on the patient's well-being and desired results. AI is a cost-effective solution for reducing the ocular problems and avoidable blindness associated with diabetes by detecting diabetic retinopathy early. CGMs, or continuous glucose monitors, have the potential to lower the price of diabetic medical care.

Hub

Commented on Public Health
Predicting Diabetes with Decision Trees in Python The Scikit-learn Python package, used with the CART method, provides binary, category, and numerical target variables. However, only binary and numerical features are currently available for the feature variables. This means that each node in the decision tree can only have two branches coming out of it, and characteristics must be true or false. The good news is that decision trees require relatively minimal data preparation. Thus, there is no need to first centre or normalize the numerical characteristics. https://statisticallyrelevant.com/decision-trees-in-python-predicting-diabetes/

Hub

Commented on AI prompt engineering hub
You learn how to configure and utilize the OpenAI API for a variety of use cases in this lesson. The course is made to be simple enough to follow even by individuals with no prior experience with Python programming. look at how high-quality huge language models may be accessed and responses can be generated by anyone. https://www.kdnuggets.com/openai-api-for-beginners-your-easy-to-follow-starter-guide

Hub

Commented on AI in education, teaching, learning
It takes a lot of work and a combination of technical know-how and strategic planning to integrate ChatGPT into an education software. This integration offers improved user experiences and cutting-edge instructional solutions thanks to a committed and experienced team, adherence to best practices, and a clearly defined project strategy. https://www.analyticsinsight.net/how-to-learn-data-analysis-from-scratch-a-guide/

Article

Commented on Mobile Digital Forensics Framework for Smartphone User Analysis
The current research examined users' statuses on smartphone applications and developed a framework for smartphone user analysis that could be used in digital forensic investigations to obtain important digital evidence from a blockchain perspective. This study examined a system that, by using these frameworks, may give digital forensic analysts vital information. When a smartphone is seized, blockchain-based digital forensics technology can be used to gather and examine evidence in accordance with smartphone forensics protocols, so effectively preventing evidence falsification or manipulation.

Poster

Commented on Agri-Solar Water Pumping Design, Energy & Environmental Analysis A Comprehensive Study in Tropical Humid Climate
The poster showes the visual detail about how solar-powered water pumping systems are designed in humid tropical climates. It is intended for use in agriculture, offering information on the environmental impact and energy efficiency of such systems. Data analysis, performance reviews, and considerations unique to the difficulties presented by the tropical humid environment are all possible components of the entire study.

Hub

Commented on Innovation & Technology Laboratory
Calling all innovators from North Macedonia! Are you ready to take your idea to the next level? Apply now for GIST Innovates: The Balkans, a 10-week pre-accelerator program taught by Arizona State University, the latest program offering from the GIST Net and the U.S. Department of State. This opportunity will empower innovators with training, mentoring, and resources to build their entrepreneurial skills and bring their ideas to market, as well as provide opportunities for teams to connect with mentors in the U.S. and build connections with U.S.-based investors and companies. Apply today! 👉 https://www.gistnetwork.org/activity/gist-innovates-balkans-2024 #GISTBalkans #GISTNetwork