Artificial intelligence (AI) and machine learning (ML), has revolutionized our world by offering innovative solutions to complex problems and improving various aspects of our lives. In the field of biomedicine, the fusion of AI and ML with biomedical image and signal processing presents transformative opportunities for healthcare. AI and ML techniques applied to biomedical image processing enable the analysis, interpretation, and extraction of valuable information from medical images. By utilizing these technologies, abnormalities such as tumors, lesions, or anatomical structures can be detected and classified with improved accuracy. For example, AI algorithms have been developed to identify and classify skin lesions in dermatology images, aiding in early skin cancer detection. Similarly, in radiology, AI-powered algorithms have shown promise in detecting fractures, and brain hemorrhages leading to enhanced diagnostic accuracy. In the realm of signal processing, AI and ML techniques play a crucial role in analyzing biomedical data such as electrocardiograms (ECG), electroencephalograms (EEG), or electromyograms (EMG). These techniques extract relevant features and patterns from signals, facilitating disease diagnosis and monitoring. For instance, AI algorithms are utilized to detect arrhythmias in ECG signals, assisting in the diagnosis of heart conditions. ML models can also analyze EEG data to identify abnormal brain activity patterns associated with epilepsy, enabling early detection and personalized treatment strategies. The integration of AI and ML in the biomedical image and signal processing holds promise for enhancing medical imaging modalities and improving image quality. AI algorithms can reduce noise, enhance resolution, and reconstruct images, providing clearer and more detailed representations of anatomical structures or biological processes. For example, AI-based denoising techniques are employed to enhance magnetic resonance imaging (MRI), improving the visibility of fine structures. The application of AI and ML in biomedical image and signal processing has the potential to revolutionize healthcare by enabling more accurate diagnostics, personalized treatments, and real-time monitoring. Extracting meaningful information from complex biomedical data empowers healthcare professionals to make informed decisions, resulting in improved patient outcomes and enhanced healthcare delivery. In conclusion, the fusion of AI and machine learning with biomedical image and signal processing presents the potential for revolutionizing healthcare. By leveraging these technologies, researchers can unlock valuable insights leading to more accurate diagnostics, personalized treatments, and improved patient care. Continued research and collaboration in this field are essential to harness the full potential of AI and ML in transforming the landscape of biomedical imaging and signal processing.

Only a couple of days back in October 24,22, Bangladesh was hit by the Twister Sitrang which caused massive decimation in the nation and individuals sufferings are likewise unexplainable. In the past, this country dealt with such storms once in every four to five years; however, in recent years, it has become more frequent, with at least one or two cyclones occurring each year. What exactly is the primary reason for this? Climate change is a pressing concern all over the world and has already shown its face to the third-world nation to which I belong . The problem posed by the climate can take many different forms, as can the solution. However, if we examine the underlying causes of everything, we eventually arrive at the conclusion that the overuse of fossil fuels has the greatest negative impact on the climate. The Intergovernmental Panel on Climate Change (IPCC) found that fossil fuel emissions account for 89% of global CO2 emissions, making them the primary cause of global warming. Carbon dioxide and other greenhouse gases that trap heat in the earth's atmosphere and contribute to climate change are released when fossil fuels are burned. In 2019, petroleum derivatives represented 74% of U.S. ozone harming substance outflows. In the United States, fossil fuels extracted from public lands account for nearly 25% of emissions. Ocean acidification, sea level rise, and extreme weather are among the climate externalities caused by fossil fuels. I believe that policy has a greater impact than individual contribution. The strategy that would work best would be to charge more for carbon emissions. A carbon price that specifies a cost that emitters must pay for their carbon dioxide emissions. There are a variety of ways carbon price policies can be structured, including a carbon tax. People or businesses that use fossil fuels will either reduce their use or look for alternatives when they have to pay such a high price. This could be a great way to lessen the crisis by lessening the use of fossil fuels. In my country, I have witnessed people struggling with hunger, losing their homes, and dealing with salt water as a result of climate change. However, the government has not played a significant role to improve the lives of these marginalized people. My great-grandfather had to move to the cities because our village was washed away by a river nearby. For this reason, I continually strive to combat climate change in my life. I'm motivated to raise funds for such a noble cause, but it requires a substantial investment. Since I am a student of science, I always try to look for a solution where technology is involved. Hydroponics, a technology-centered on agriculture, enables coastal residents to grow plants even in extreme conditions. I think it would be a good idea to provide hydroponic setups to poor people in the affected areas. Hydroponics are environmentally friendly, require less water which can be reused. When I raise money, I want to offer every household free installation of hydroponic system to give them a chance to fight back.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I do research in biomedical imaging and machine learning-based applications for medical data such as EEG and MRI. My research theme is poor people who suffer from inadequate medical care because of not having enough money. I am working to develop a low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care, it has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. I am happy that research is dedicated to such a noble cause. I also have a strong interest in working with children with autism. I published an article where I developed a deep neural network (DNN) for ASD classification. After my daughter was born, I become sympathetic to the mothers seeing their struggles in working with special children. If we could identify autism earlier the child can be normal with minimal care and medication. It gives me heavenly pleasure working for those beautiful souls. Studying Engineering provides me with a sense of satisfaction and strengthens my desire to work for people. My grandmother is my inspiration. She was a serious patient with dementia, and the doctor at that time could do little to alleviate her pain. At one stage, she had forgotten all of us, the pain she experienced is unexplainable. I decided to research brain and neural activity to increase my understanding of neural diseases so that I can contribute to people’s well-being. For my research, I must record clinical data such as MRI, and EEG of elderly people dealing, which reminds me of my grandmother.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I do research in biomedical imaging and machine learning-based applications for medical data such as EEG and MRI. My research theme is poor people who suffer from inadequate medical care because of not having enough money. I am working to develop a low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care, it has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. I am happy that research is dedicated to such a noble cause. I also have a strong interest in working with children with autism. I published an article where I developed a deep neural network (DNN) for ASD classification. After my daughter was born, I become sympathetic to the mothers seeing their struggles in working with special children. If we could identify autism earlier the child can be normal with minimal care and medication. It gives me heavenly pleasure working for those beautiful souls. Studying Engineering provides me with a sense of satisfaction and strengthens my desire to work for people. My grandmother is my inspiration. She was a serious patient with dementia, and the doctor at that time could do little to alleviate her pain. At one stage, she had forgotten all of us, the pain she experienced is unexplainable. I decided to research brain and neural activity to increase my understanding of neural diseases so that I can contribute to people’s well-being. For my research, I must record clinical data such as MRI, and EEG of elderly people dealing, which reminds me of my grandmother. We know most of the time religion is not a choice for us, but we perceive religion from the family we were born. All religion has the basics that are to speak about humanity. Islam formed such that it can whisper love among a morally destroyed civilization. It teaches us to select the path that is straight and close to humanity. I believe the more I do good the more I will become close to Allah. So, I always keep cautious that nobody gets hurt by my words and deeds. A Muslim also respects other views and filling, I believe most of the conflicts could have been avoided if we endure and pay heed to others. I feel a send sense of honor for being a Muslim girl.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I do research in biomedical imaging and machine learning-based applications for medical data such as EEG and MRI. My research theme is poor people who suffer from inadequate medical care because of not having enough money. I am working to develop a low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care, it has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. I am happy that research is dedicated to such a noble cause. I also have a strong interest in working with children with autism. I published an article where I developed a deep neural network (DNN) for ASD classification. After my daughter was born, I become sympathetic to the mothers seeing their struggles in working with special children. If we could identify autism earlier the child can be normal with minimal care and medication. It gives me heavenly pleasure working for those beautiful souls. Studying Engineering provides me with a sense of satisfaction and strengthens my desire to work for people.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I do research in biomedical imaging and machine learning-based applications for medical data such as EEG and MRI. My research theme is poor people who suffer from inadequate medical care because of not having enough money. I am working to develop a low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care, it has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. I am happy that research is dedicated to such a noble cause. I also have a strong interest in working with children with autism. I published an article where I developed a deep neural network (DNN) for ASD classification. After my daughter was born, I become sympathetic to the mothers seeing their struggles in working with special children. If we could identify autism earlier the child can be normal with minimal care and medication. It gives me heavenly pleasure working for those beautiful souls. Studying Engineering fills me with satisfaction, and it strengthens my desire to work for people. I think science is the future if we want to secure a sustainable life, we need to look for solutions by exploring science. For example, after COVID need for more machine-learning applications is evident. We have seen that shortage of equipment and healthcare professionals had a negative impact on overall patient care, resulting in people dying in numbers. Many developed countries measured the covid prone areas with the help of data science and ML and successfully imposed area-based lockdowns with minimal effect on larger communities. A study at JinYanTan hospital in Wuhan, conducted a study on 287 patients, incorporated three machine learning techniques, and concluded that an ML-based prediction model has the potential to facilitate severe COVID-19 patient ranges from identification, applying timely treatment, and optimization of resources.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I do research in biomedical imaging and machine learning-based applications for medical data such as EEG and MRI. My research theme is poor people who suffer from inadequate medical care because of not having enough money. I am working to develop a low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care, it has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. I am happy that research is dedicated to such a noble cause. I also have a strong interest in working with children with autism. I published an article where I developed a deep neural network (DNN) for ASD classification. After my daughter was born, I become sympathetic to the mothers seeing their struggles in working with special children. If we could identify autism earlier the child can be normal with minimal care and medication. It gives me heavenly pleasure working for those beautiful souls. Studying Engineering fills me with satisfaction, and it strengthens my desire to work for people.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I believe technology can shape our future in a positive way. I am passionate about machine learning and its application to the real world. After COVID the need for more machine learning applications is evident. We have seen that shortage of equipment and health care professionals had a negative impact on overall patients' care, resulting in people dying in numbers. Many developed countries measured the COVID prone areas with the help of data science and ML, and successfully imposed area-based lockdown with minimal effect on larger communities. A study at JinYanTan hospital in Wuhan, conducted study on 287 patients, incorporated three machine learning techniques and concluded that ML based prediction model has the potential to facilitate severe COVID-19 patient ranges from identification, applying timely treatment and optimization of resources. I do research in biomedical imaging and develop machine learning based applications for medical data such as EEG and MRI. My goal is to do medical imaging research and give something back to humanity. My research theme is poor people who suffer from inadequate medical care. I am working to develop low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care. The research I'm doing has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. It's my mother's dream, and I am happy that my work and research are dedicated to such a noble cause.

Hi! I am a graduate student at the University of Oklahoma majoring in Electrical and Computer Engineering. I do research in biomedical imaging and develop machine learning based applications for medical data such as EEG and MRI. My goal is to do medical imaging research and give something back to humanity. My research theme is poor people who suffer from inadequate medical care. I am working to develop low-cost, machine-driven, automated diagnosis of illnesses and disabilities so that the poor have access to adequate medical care. The research I'm doing has the potential to meet future needs where people don't have to go to a diagnostic center and can get by with inexpensive apps. It's my mother's dream, and I am happy that my work and research are dedicated to such a noble cause. I also have a strong interest in working with children with autism. Published an article in a journal - Developed a deep neural network (DNN) for ASD classification. After my daughter was born, I became sympathetic to the mothers seeing their struggles. Specifically, when I see mothers dealing with a special child. I wonder if we could identify autism earlier there will be a fair chance that the child can be normal with minimal care and medication. So, working with autism and neural activity is my second scope of research. It gives me heavenly pleasure working for those beautiful souls. Diversity in the workplace is important to me. Having people with divergent backgrounds, characteristics, different skills, experiences and abilities, working in such a place helps me to be more innovative and creative. When it comes to learning technology, it allows you to broaden your horizons of experience and knowledge and see different perspectives that lead to great ideas. I also enjoy working with people of diverse countries and cultures. Some people may come from countries that I don't know. Working with them helps me understand different cultures. Also, getting to know people of different races, genders, and cultures help me to empathize with different races. It makes me more human than I am. Companies also get lots of advantages from recruiting people of divergent culture. Like growth in productivity and innovations. According to research, when there is 1% racial diversity similarity present among the upper management and lower management, productivity increases by $729 to $1590 per employee per year. It is a win-win situation for both employer and employees. I think every company should incorporate diversity into the workplace.