This paper presents a multi-objective version of the Cat Swarm Optimization Algorithm called the Grid-based Multi-objective Cat Swarm Optimization Algorithm (GMOCSO). Convergence and diversity preservation are the two main goals pursued by modern multi-objective algorithms to yield robust results. To achieve these goals, we first replace the roulette wheel method of the original CSO algorithm with a greedy method. Then, two key concepts from Pareto Archived Evolution Strategy Algorithm (PAES) are adopted: the grid system and double archive strategy. Several test functions and a real-world scenario called the Pressure vessel design problem are used to evaluate the proposed algorithm's performance. In the experiment, the proposed algorithm is compared with other well-known algorithms using different metrics such as Reversed Generational Distance, Spacing metric, and Spread metric. The optimization results show the robustness of the proposed algorithm, and the results are further confirmed using statistical methods and graphs. Finally, conclusions and future directions were presented..

The headlines seem to write themselves (if that cliché is allowed anymore in the age of ChatGPT and generative AI). But that is a metaphor, right? When I first saw Michael Saylor's Twitter account, I wasn't sure. Saylor is an entrepreneur, tech executive, and former billionaire. Once reportedly the richest man in the Washington, DC, area, he lost most of his 7 billion net worth in 2000 when, in his mid-30s, he reached a settlement with the US Securities and Exchange Commission after it brought charges against him and two of his colleagues at a company called MicroStrategy for inaccurate reporting of their financial results.

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements.

Artificial Intelligence-based (AI) analysis of large, curated medical datasets is promising for providing early detection, faster diagnosis, and more effective treatment using low-power Electrocardiography (ECG) monitoring devices information. However, accessing sensitive medical data from diverse sources is highly restricted since improper use, unsafe storage, or data leakage could violate a person's privacy. This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG from 12-lead sensor arrays collected from six heterogeneous sources. We evaluated the capacity of the resulting models to achieve equivalent performance compared to state-of-the-art models trained in a Centralized Learning (CL) fashion. Moreover, we assessed the performance of our solution over Independent and Identical distributed (IID) and Non-IID federated data. Our methodology involves machine learning techniques based on Deep Neural Networks and Long-Short-Term Memory models. It has a robust data preprocessing pipeline with feature engineering, selection, and data balancing techniques. Our AI models demonstrated comparable performance to models trained using CL, IID, and Non-IID approaches. They showcased advantages in reduced complexity and faster training time, making them well-suited for cloud-edge architectures.

Like most kids his age, 15-year-old Hassan spent a lot of time online. Before the pandemic, he liked playing football with local kids in his hometown of Burewala in the Punjab region of Pakistan. But Covid lockdowns made him something of a recluse, attached to his mobile phone. "I just got out of my room when I had to eat something," says Hassan, now 18, who asked to be identified under a pseudonym because he was afraid of legal action. From his childhood bedroom, the high schooler was working in the global artificial intelligence supply chain, uploading and labeling data to train algorithms for some of the world's largest AI companies.

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools).

Your browser does not support the video element. Even if you've never played, by now you know of "Fortnite." Five years ago the battle royale debuted for millions of people across the world, becoming one of gaming's biggest titles and even launching some of its top players into new stratospheres of celebrity. But what's more interesting is what Fortnite could yet become -- and how the game could reshape the internet as we know it. DrLupo has made millions while streaming to his 4.5 million followers on Twitch and another 1.8 million subscribers on YouTube.

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation (EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.

Ideas about what constitutes "beauty" are complex, subjective, and by no means limited to physical appearances. Elusive though it is, everyone wants more of it. That means big business and increasingly, people harnessing algorithms to create their ideal selves in the digital and, sometimes, physical worlds. In this episode, we explore the popularity of beauty filters, and sit down with someone who's convinced his software will show you just how to nip and tuck your way to a better life. This episode was reported by Tate Ryan-Mosley, and produced by Jennifer Strong, Emma Cillekens, Karen Hao and Anthony Green. Strong: Beauty has always been one of society's greatest obsessions. And for as long as we've worshipped it… we've also found ways to change and enhance it. From makeup and clothes... to airbrushing photos… or a surgical nip and tuck. Strong: You may not realize it...but this technology is right at your fingertips.

An artificial intelligence voice analysis tool can help diagnose COVID-19 in asymptomatic patients, according to its manufacturer, Vocalis Health. The technology -- called VocalisCheck -- works by comparing a person's voice sample to a COVID-19-positive voice composite. VocalisCheck assesses their risk level of testing positive for COVID-19 and whether they require further testing. According to the company, early study results show that VocalisCheck had a sensitivity of 87% and specificity of of 53%, when used alone, adding "even better" results were achieved when combined with a symptom questionnaire. "Over time, we will collect more and more data, which can strengthen the AI and make the vocal biomarker even more accurate," Shady Hassan, MD, co‐founder, chief medical and chief operating officer of Vocalis Health, told Healio Primary Care.