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Category: information science – Page 140

Choosing interesting dissertation topics in ML is the first choice of Master’s and Doctorate scholars nowadays. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Selecting and working on a dissertation topic in machine learning is not an easy task as machine learning uses statistical algorithms to make computers work in a certain way without being explicitly programmed. The main aim of machine learning is to create intelligent machines which can think and work like human beings. This article features the top 10 ML dissertations for Ph.D. students to try in 2022.

Text Mining and Text Classification: Text mining is an AI technology that uses NLP to transform the free text in documents and databases into normalized, structured data suitable for analysis or to drive ML algorithms. This is one of the best research and thesis topics for ML projects.

Recognition of Everyday Activities through Wearable Sensors and Machine Learning: The goal of the research detailed in this dissertation is to explore and develop accurate and quantifiable sensing and machine learning techniques for eventual real-time health monitoring by wearable device systems.

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“If we get a similar hit rate in detecting texture in tumors, the potential for early diagnosis is huge,” says scientist.

Researchers at University College London.

The potentially early-stage fatal tumors in humans could be noticed by the new x-ray method that collaborates with a deep-learning Artificial Intelligence (AI) algorithm to detect explosives in luggages, according to a report published by MIT Technology Review on Friday.

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“Neuromorphic computing could offer a compelling alternative to traditional AI accelerators by significantly improving power and data efficiency for more complex AI use cases, spanning data centers to extreme edge applications.”

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Can computer systems develop to the point where they can think creatively, identify people or items they have never seen before, and adjust accordingly — all while working more efficiently, with less power? Intel Labs is betting on it, with a new hardware and software approach using neuromorphic computing, which, according to a recent blog post, “uses new algorithmic approaches that emulate how the human brain interacts with the world to deliver capabilities closer to human cognition.”

While this may sound futuristic, Intel’s neuromorphic computing research is already fostering interesting use cases, including how to add new voice interaction commands to Mercedes-Benz vehicles; create a robotic hand that delivers medications to patients; or develop chips that recognize hazardous chemicals.

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Drake’s equation may look complicated, but its principles are really rather simple. It states that, in a galaxy as old as ours, the number of civilizations that are detectable by virtue of them broadcasting their presence must equate to the rate at which they arise, multiplied by their average lifetime.

Putting a value on the rate at which civilizations occur might seem to be guesswork, but Drake realized that it can be broken down into more tractable components.

He stated that the total rate is equal to the rate at which suitable stars are formed, multiplied by the fraction of those stars that have planets. This is then multiplied by the number of planets that are capable of bearing life per system, times the fraction of those planets where life gets started, multiplied by the fraction of those where life becomes intelligent, times the fraction of those that broadcast their presence.

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The very first industrial revolution historically kicked off with the introduction of steam-and water-powered technology. We have come a long way since then, with the current fourth industrial revolution, or Industry 4.0, being focused on utilizing new technology to boost industrial efficiency.

Some of these technologies include the internet of things (IoT), cloud computing, cyber-physical systems, and artificial intelligence (AI). AI is the key driver of Industry 4.0, automating to self-monitor, interpret, diagnose, and analyze all by themselves. AI methods, such as machine learning (ML), (DL), processing (NLP), and computer vision (CV), help industries forecast their maintenance needs and cut down on downtime.

However, to ensure the smooth, stable deployment and integration of AI-based systems, the actions and results of these systems must be made comprehensible, or, in other words, “explainable” to experts. In this regard, explainable AI (XAI) focuses on developing algorithms that produce human-understandable results made by AI-based systems. Thus, XAI deployment is useful in Industry 4.0.

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You’re on the PRO Robots channel, and today we’re bringing you some high-tech news. Robots from Boston Dynamics will get advanced artificial intelligence, neural networks will be able to translate the language of all animals, incredibly fast nanorobots will travel inside the human body, a robot-surgeon will perform an operation on the ISS. See these and other technology news in one video right now!

0:00 Intro.
0:28 Robots from Boston Dynamics get advanced artificial intelligence.
1:52 AI will never be intelligent.
2:50 Earth Species Project hopes to develop a neural network that can decipher animal language.
3:16 Species Project decides to go around and create an algorithm.
4:07 A gadget to control your smart home with your mind.
5:04 Nanobots.
5:19 The world’s fastest bowel robot.
6:10 Robots will join the U.S. space forces.
6:47 Surgical robot to be tested on ISS
7:37 GITAI News.
7:59 The first launch in NASA’s Artemis lunar mission.
8:34 Super Heavy rocket successfully passes first static firing test.
8:57 Gigafactory in Canada.
9:22 Baidu says its Jidu robot car autopilot will be a generation ahead of Tesla’s autopilot.
10:02 A system that can calculate the optimal end design and calculate the best trajectory for grabbing objects of any shape.
10:25 A drone to search for gold and jewelry.
11:22 Engineers have trained a drone with 12 rotary screws to manipulate objects.
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#prorobots #technology #roboticsnews.

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Researchers have developed a camera that uses a thin microlens array and new image processing algorithms to capture 3D information about objects in a scene with a single exposure. The camera could be useful for a variety of applications such as industrial part inspection, gesture recognition and collecting data for 3D display systems.

“We consider our camera lensless because it replaces the bulk lenses used in conventional cameras with a thin, lightweight microlens array made of flexible polymer,” said research team leader Weijian Yang from the University of California, Davis. “Because each microlens can observe objects from different viewing angles, it can accomplish complex imaging tasks such as acquiring 3D information from objects partially obscured by objects closer to the camera.”

In the journal Optics Express, Yang and first author Feng Tian, a doctoral student in Yang’s lab, describe the new 3D camera. Because the camera learns from existing data how to digitally reconstruct a 3D scene, it can produce 3D images in real time.

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For all of history, there’s been an underlying but unspoken assumption about the laws that govern the Universe: If you know enough information about a system, you can predict precisely how that system will behave in the future. The assumption is, in other words, deterministic. The classical equations of motion — Newton’s laws — are completely deterministic. The laws of gravity, both Newton’s and Einstein’s, are deterministic. Even Maxwell’s equations, governing electricity and magnetism, are 100% deterministic as well.

But that picture of the Universe got turned on its head with a series of discoveries that began in the late 1800s. Starting with radioactivity and radioactive decay, humanity slowly uncovered the quantum nature of reality, casting doubt on the idea that we live in a deterministic Universe. Predictively, many aspects of reality could only be discussed in a statistical fashion: where a set of probable outcomes could be presented, but which one would occur, and when, could not be precisely established. The hopes of avoiding the necessity of “quantum spookiness” was championed by many, including Einstein, with the most compelling alternative to determinism put forth by Louis de Broglie and David Bohm. Decades later, Bohmian mechanics was finally put to an experimental test, where it failed spectacularly. Here’s how the best alternative to the spooky nature of reality simply didn’t hold up.

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According to a University of Portsmouth study, a new physics law could allow for the early prediction of genetic mutations.

The study discovers that the second law of information dynamics, or “infodynamics,” behaves differently from the second law of thermodynamics. This finding might have major implications for how genomic research, evolutionary biology, computing, big data, physics, and cosmology develop in the future.

Lead author Dr. Melvin Vopson is from the University’s School of Mathematics and Physics. He states “In physics, there are laws that govern everything that happens in the universe, for example how objects move, how energy flows, and so on. Everything is based on the laws of physics. One of the most powerful laws is the second law of thermodynamics, which establishes that entropy – a measure of disorder in an isolated system – can only increase or stay the same, but it will never decrease.”

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A groundbreaking mathematical equation that could transform medical procedures, natural gas extraction, and plastic packaging production in the future has been discovered.

The new equation, developed by scientists at the University of Bristol, indicates that diffusive movement through permeable material can be modeled exactly for the very first time. It comes a century after world-leading physicists Albert Einstein and Marian von Smoluchowski derived the first diffusion equation, and marks important progress in representing motion for a wide range of entities from microscopic particles and natural organisms to man-made devices.

Until now, scientists looking at particle motion through porous materials, such as biological tissues, polymers, various rocks and sponges have had to rely on approximations or incomplete perspectives.

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