Abstract--This paper introduces a novel framework for enhancing Random Forest classifiers by integrating probabilistic feature sampling and hyperparameter tuning via Simulated Annealing. The proposed framework exhibits substantial advancements in predictive accuracy and generalization, adeptly tackling the multifaceted challenges of robust classification across diverse domains, including credit risk evaluation, anomaly detection in IoT ecosystems, early-stage medical diagnostics, and high-dimensional biological data analysis. T o overcome the limitations of conventional Random Forests, we present an approach that places stronger emphasis on capturing the most relevant signals from data while enabling adaptive hyperparameter configuration. The model is guided towards features that contribute more meaningfully to classification and optimizing this with dynamic parameter tuning. The results demonstrate consistent accuracy improvements and meaningful insights into feature relevance, showcasing the efficacy of combining importance aware sampling and metaheuristic optimization. RFs are widely used ensemble learning methods known for their robustness, interpretability, scalability and performance across diverse machine learning tasks.

Study participants successfully grasped and transported two objects at once without dropping them. Prosthetic hands have long struggled to replicate the dexterity and functionality of natural hands, often limiting users to a single grasp function at a time. This limitation has made everyday tasks, such as typing on a keyboard or braiding hair, challenging for those who rely on prosthetics. However, a groundbreaking study from Florida Atlantic University (FAU) offers promising advancements that could transform the experience of prosthetic hand users. Researchers at FAU's College of Engineering and Computer Science, in collaboration with the Charles E. Schmidt College of Science, conducted an innovative study that combined haptic feedback, electromyogram control and a wearable soft robotic armband.

Researchers captured drone footage of blacktip sharks evading a 12-foot-long hammerhead shark in Florida. Researchers have captured dramatic drone footage of blacktip sharks quickly evading a 12-foot-long hammerhead shark by swimming into shallow waters off Florida's coast. The drone footage captured by researchers with Florida's Atlantic University is the first evidence of large adult sharks using the shallows to flee predators. In the dramatic footage, the adult blacktop sharks are seen fleeing for shallow waters when faced with the hammerhead. The hammerhead shark was caught approaching the smaller blacktip sharks off the coast of Florida.

Boca Raton, FL – Florida Atlantic University is launching four new master's degree programs for working professionals, including an expansion of the state's first Master of Science with Major in Artificial Intelligence (AI). The FAU Board of Trustees recently approved the programs in information technology management, supply chain management and business analytics, in addition to AI. The four degree programs, already offered to full-time students, will be available online and in-person to working professionals and are expected to begin in the fall. The master's in AI degree, first announced in 2019 for full-time students, is part of an innovative program that covers AI foundations and technologies. Students will develop new skills for jobs not yet defined, allowing them to join a first-generation workforce with significant earning potential.

Researchers from Florida Atlantic University's College of Engineering and Computer Science have received a four-year, $1 million grant from the National Science Foundation for a project to make the master's degree in artificial intelligence (AI) accessible to high-achieving, low-income students. The accelerated five-year bachelor's degree in science and master's degree in AI program is designed to adapt curricular and co-curricular support to enable students to complete their degrees in AI, autonomous systems or machine learning, which are critically important areas needed to advance America's global competitiveness and national security. "Artificial intelligence is transforming every walk of life from business to healthcare and enabling us to rethink how we analyze data, integrate massive amounts of information and make informed decisions that impact society, the economy and governance," said Stella Batalama, Ph.D., dean of FAU's College of Engineering and Computer Science and a co-principal investigator of the grant. "This important grant from the National Science Foundation will allow us to recruit and train talented and diverse students who are economically disadvantaged and provide them with a unique opportunity to pursue graduate education in an exciting and burgeoning field." By preparing increased numbers of high-achieving, low-income students to become engineers in these fields, this project addresses the need for growing a more diverse STEM (science, technology, engineering and mathematics) research population.

Currently, there are no specific guidelines on the most effective materials and designs for facemasks to minimize the spread of droplets from coughs or sneezes to mitigate the transmission of COVID-19. While there have been prior studies on how medical-grade masks perform, data on cloth-based coverings used by the vast majority of the general public are sparse. Research from Florida Atlantic University's College of Engineering and Computer Science, just published in the journal Physics of Fluids, demonstrates through visualization of emulated coughs and sneezes, a method to assess the effectiveness of facemasks in obstructing droplets. The rationale behind the recommendation for using masks or other face coverings is to reduce the risk of cross-infection via the transmission of respiratory droplets from infected to healthy individuals. Researchers employed flow visualization in a laboratory setting using a laser light sheet and a mixture of distilled water and glycerin to generate the synthetic fog that made up the content of a cough-jet.

We've already seen several dog-like quadruped robots, which move by walking with their four backward-bending legs. Researchers at Florida Atlantic University are now getting in on the act, with their artificially-intelligent (and dog-headed) Astro robot. First of all, Astro's style of locomotion is intended to be more than just an attention-getter. Like the other robo-dogs that are on the market or in development, it's able to withstand attempts at knocking it over, plus it's well-suited to traversing rough terrain – such as it might encounter in search-and-rescue operations, or when surveying disaster sites. What reportedly makes Astro special, however, is the set of Nvidia Jetson TX2 graphics processing units contained within its 3D-printed, Doberman pinscher-inspired head.

With his 3-D printed doberman-like head, robot dog Astro may look like something out of a Black Mirror episode -- but this clever canine may be our new best friend. Powered by artificial intelligence technology, the metallic mutt can presently respond to simple commands like'sit', 'stand' and'lie down'. However, by training him in thousands of different scenarios, this robot dog is capable of learning new tricks. His developers expect that he will eventually be able to recognise different languages, hand signals, people and other dogs -- and even team up with drones. Astro is intended to help security forces sniff out prohibited items and first responders scour disaster sites -- but he might even find work as a guide dog.

South Korea is developing robots that mimic wildlife adapted for all environments on Earth for military warfare. The nature inspired technology, known as biomimetics, will form part of the country's future weapons systems and help its soldiers in battles. Robot designs inspired by birds, snakes and marine species aim to cover both surveillance and combat via sea, land and sky. It is an attempt to catch up with neighbouring countries such as China and Russia who have made huge advances in the application of the technology, said a defence agency personnel. South Korea is developing a range of robots that mimic wildlife adapted for all environments on Earth for military warfare.

IMAGE: This is Taghi M. Khoshgoftaar, Ph.D., co-author and Motorola Professor in FAU's Department of Computer and Electrical Engineering and Computer Science. Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud.