By Raghavendra Goud Vaggu

The severity of tuberculosis (TB) makes it a troubling crisis across the globe, especially as it is responsible for millions of deaths around the world. According to the World Health Organisation (WHO), TB was responsible for 1.6 million deaths in 2021, making it the 13th biggest killer and second leading infectious killer only after COVID-19 that year. With 10.6 million people falling ill in 2021 to the dreaded disease, and patients cutting across all demographics, there is need for vigilance.

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The rise of computer-aided diagnostics has certainly added impetus to the drive for better TB diagnosis, especially because of better medical imaging that gives radiologists more precise interpretation of the patient's chest, blood, spine, or brain, depending on the part of the body that is affected. One of such tools is the CAD model which offers precise diagnosis of the TB cavity and clearly displays areas of interest when observing the chest x-ray image. This is a huge improvement on preexisting CAD systems which could not identify TB cavities, as a result of the lung field's superimposed anatomic parts.

Looking ahead in TB diagnosis and treatment

Within the framework of modern TB analysis, the place of data cannot be overemphasised. Fully automated CAD systems are being experimented for their great features, including handcrafted systems and deep features. These systems use pre-trained CNN frameworks and supervised learning to probe the parts of the body that are of interest using obtained data and processing such data to reach conclusive diagnosis. This also comes with the ongoing conversation around the difference between supervised and unsupervised learning and how the choice of the future can shape TB diagnosis. More advanced CAD systems will likely emerge in the future, powered by superior AI.

Raghavendra Goud Vaggu, Global CEO, Empe Diagnostics

(DISCLAIMER: The views expressed are sole of the author and ETHealthworld does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person/organisation directly or indirectly)

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The Evolution of Machine Learning In TB Diagnostics: Unlocking Patterns and Insights - ETHealthWorld

In an era marked by the rapid advancement of artificial intelligence (AI) technologies, the issues of transparency, ethics, and accessibility have taken center stage. While AI solutions have undoubtedly propelled the field forward, there remains a critical need to address issues related to fairness and accessibility. Recognizing this imperative, Fujitsu, a leading developer of AI technologies in Japan, has embarked on a groundbreaking commitment to open-source AI in collaboration with the Linux Foundation. This initiative addresses these challenges and aims to provide accessible solutions that can benefit a broader range of developers and industries.

Existing AI solutions have undoubtedly driven progress in the field, but they often fall short when addressing issues related to fairness and accessibility. Fujitsus latest endeavor, in partnership with the Linux Foundation, seeks to bridge these gaps and offer practical solutions that can empower developers and industries alike.

One of the cornerstones of this initiative is the automated machine learning project known as SapientML. This innovative project offers the capability to rapidly create highly efficient machine learning models and custom algorithms for a companys unique data. By expediting the development process and facilitating the fine-tuning of precise models, SapientML plays a pivotal role in accelerating progress in the AI field. It significantly reduces time-to-market for AI solutions, enabling companies to bring their innovations to the world more swiftly and effectively.

The second project, Intersectional Fairness, addresses a crucial aspect of AI development mitigating biases within AI systems. This technology is designed to excel at identifying subtle biases that may emerge at the intersection of attributes like gender, age, and ethnicity. Overcoming these often overlooked biases is paramount in creating fair and ethical AI systems that serve diverse populations equitably. Intersectional Fairness technology aligns with societal values and ethical standards, ensuring that AI systems are inclusive and impartial.

The efficacy of these solutions is further underscored by their metrics, which provide tangible evidence of their capabilities. SapientMLs ability to swiftly generate optimized machine learning models and tailored code has a transformative impact on AI development, offering a competitive edge in the industry. On the other hand, Intersectional Fairness technology not only identifies hidden biases but also actively contributes to eliminating them, fostering the creation of AI systems that are advanced technologically and ethically sound.

In conclusion, Fujitsus unwavering commitment to open-source AI, in collaboration with the Linux Foundation, heralds a new era in the development of AI technologies. This initiative goes beyond simply addressing the pressing issues of transparency and fairness; it also democratically opens access to cutting-edge AI technologies. As AI continues to shape our modern world, collective open-source efforts exemplify AIs immense potential to be a tool for global innovation while adhering to rigorous ethical standards. The future of AI embraces inclusivity, accessibility, and fairness for all, and Fujitsus initiatives are leading the way toward this bright future.

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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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Fujitsu and the Linux Foundation Launch Fujitsus Automated Machine Learning and AI Fairness Technologies: Pioneering Transparency, Ethics, and...

This article gathers 5 machine-learning algorithms used in Python for analyzing and making predictions from data

Machine learning algorithms are essential for deriving knowledge from data and generating predictions. There are a number of widely used machine learning algorithms in Python that offer solid tools for addressing a variety of issues. These algorithms are made to extract patterns and correlations from data, allowing computers to reason and forecast the future. This postwill examine five well-known machine-learning algorithms usedin Python.

1. Naive Bayes- The classification approach used by this algorithm, which is based on the Bayes theorem, works by assuming that characteristics belonging to the same class are unaffected by features belonging to other types. Even though the elements are interdependent, the algorithm takes that they are unrelated. This approach provides a model that performs admirably with enormous datasets.

2. Random Forest- It essentially exemplifies an ensemble learning approach for classification, regression, and other issues that works by assembling a variety of decision trees during the training phase. Each decision tree is assigned a class in Random Forest, which categorizes objects based on qualities. The type that reports the most trees is then selected using this algorithm.

3. Linear Regression- It aids in result prediction while taking into account independent variables. The linear link between independent and dependent variables is established with the aid of this ML technique. It basically implies that it illustrates how the value of the independent variables affects the dependent variable.

4. Back-propagation- By changing the weights of the input signals, this algorithm may create the necessary output signals by designing supplied functions. This algorithm for supervised learning is employed in the classification and regression processes. By using the gradient descent or delta rule techniques, back-propagation determines the error function values with the lowest minimums. It is how the method determines the necessary weights to reduce or eliminate error functions.

5. KNN, or K-nearest Neighbours- It can categorize the data points by analyzing the labels of the data points that are present around the target data points and making predictions. Both classification and regression tasks require KNN. It is a method for supervised learning that is used to identify patterns in data and find anomalies.

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5 Machine Learning Algorithms Commonly Used in Python - Analytics Insight

While AI has made tremendous progress and has become a valuable tool in many domains, it is not a replacement for humans unique qualities and capabilities. The most effective approach, in many cases, involves humans working alongside AI, leveraging each others strengths to achieve the best outcomes. There are fundamental differences between human and artificial intelligence, and there are tasks and domains where human intelligence remains superior.

Humans can think creatively, imagine new concepts, and innovate. AI systems are limited by the data and patterns theyve been trained on and often struggle with truly novel and creative tasks. However, the question is, can an average human outperform the AI model?

Researchers tried to compare the creativity of humans (n= 256) with that of three current AI chatbots, ChatGPT3.5, ChatGPT4, and Copy.AI, by using the alternate uses task (AUT), which is a divergent thinking task. It is a cognitive method used in psychology and creativity research to assess an individuals ability to generate creative and novel ideas in response to a specific stimulus. These tasks measure a persons capacity for divergent thinking, which is the ability to think broadly and generate multiple solutions or ideas from a single problem.

Participants were asked to generate uncommon and creative uses for everyday objects. AUT consisted of four tasks with objects: rope, box, pencil, and candle. The human participants were instructed to provide ideas qualitatively but not depend solely on the quantity. The chatbots were tested 11 times with four object prompts in different sessions. The four objects were tested only once within that session.

They collected subjective creativity or originality ratings from six professionally trained humans to evaluate the results. The order in which the responses within object categories were presented was randomized separately for each rater. The scores of each rater were averaged across all the responses a participant or chatbot in a session gave to an object, and the final subjective scores for each object were formed by averaging the six raters scores.

On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceeded those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to replicate or surpass with AI technology fully.

However, AI technology is rapidly developing, and the results may be different after half a year. Based on the present study, the clearest weakness in human performance lies in the relatively high proportion of poor-quality ideas, which were absent in chatbot responses. This weakness may be due to normal variations in human performance, including failures in associative and executive processes and motivational factors.

Check out thePaper.All Credit For This Research Goes To the Researchers on This Project. Also,dont forget to joinour 30k+ ML SubReddit,40k+ Facebook Community,Discord Channel,andEmail Newsletter, where we share the latest AI research news, cool AI projects, and more.If you like our work, you will love our newsletter..

Arshad is an intern at MarktechPost. He is currently pursuing his Int. MSc Physics from the Indian Institute of Technology Kharagpur. Understanding things to the fundamental level leads to new discoveries which lead to advancement in technology. He is passionate about understanding the nature fundamentally with the help of tools like mathematical models, ML models and AI.

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Can AI Outperform Humans at Creative Thinking Task? This Study Provides Insights into the Relationship Between Human and Machine Learning Creativity -...

Deep learning approaches have potential to substantially reduce the astronomical costs and long timescales involved in drug discovery. KarmaDock proposes a deep learning workflow for ligand docking that shows improved performance against both benchmark cases and in a real-world virtual screening experiment.

Drug discovery is a long and arduous process that is staggeringly expensive the average estimated time needed to take a new drug from discovery to launch is 1012 years1, at a high cost of ~US$2.2 billion per drug2, which is a major problem considering that this process is also plagued by low hit rates. Computer-aided drug discovery (CADD) can substantially aid this process3, including both by predicting how a range of drug-like ligands would bind to a given drug-target (virtual screening) using docking algorithms, as well as predicting the corresponding binding free energies of the docking predicted poses, which are a measure of the strength with which the ligand binds to its target. However, despite significant progress in this area, challenges remain, including (1) the quality of the binding poses predicted, which is crucial for rational drug discovery, a process that is complicated by the presence of error, non-linearity, and randomness4; (2) the precision and accuracy of the predicted binding free energies for those poses there can be, for instance, significant variation in the pose ranking for the same ligand/target combination between docking approaches and (3) the speed of the approach, which is particularly an issue in the face of increasing library sizes. That is, computational approaches need to be both efficient enough to be able to perform ultra-large docking on libraries that can reach billions of compounds5, without significantly compromising the quality of the binding pose and free energy predictions. Such huge libraries are out of the scope of conventional CADD approaches, but are an ideal target for deep-learning (DL) approaches5, which typically perform better than traditional shallow machine learning techniques (or even deep learning approaches with expert descriptors) when processing large data sets6. However, even DL approaches face challenges optimizing both accuracy and computational speed, due to the inherent complexity of the problem, as well as the degree of seeming randomness involved4. Writing in Nature Computational Science Xujun Zhang and colleagues7 propose KarmaDock, a DL approach for ligand docking, showing both improved speed and accuracy compared to benchmark data sets, as well as performing well in a real-world virtual screening project where it was used to discover experimentally validated active inhibitors of LTK, which is a target for the treatment of non-small-cell lung cancer8.

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Progress in using deep learning to treat cancer - Nature.com