Categories
Applied Innovation

Precision Medicine and Health: Unraveling Chronic Diseases with Advanced Technologies

Categories
Applied Innovation

Precision Medicine and Health: Unraveling Chronic Diseases with Advanced Technologies

Recent years have seen incredible progress in the healthcare industry because of innovative research and state-of-the-art technology. Precision medicine represents a novel strategy at the vanguard of medical development that holds the potential to revolutionize the understanding, diagnosis, and treatment of chronic illnesses.

Precision medicine acknowledges that a multitude of intricate elements, such as our genetic composition, lifestyle decisions, and living environment, interact to determine our overall health. Precision medicine aims to deliver a more customised and efficient approach to healthcare as opposed to using a one-size-fits-all method. Its main goal is to protect and enhance health by carefully evaluating these many components and adjusting actions as necessary.

Precision medicine takes behavioural and environmental factors into account in addition to genetic considerations. Healthcare professionals may create individualised treatment programmes that are not only successful but also precisely tailored to each patient’s specific needs thanks to this comprehensive approach.

A phrase that is frequently used synonymously with precision medicine is “precision health.” Precision health has a more all-encompassing strategy, whereas precision medicine concentrates on tailored disease risks and treatment approaches. Beyond the walls of a hospital or doctor’s office, it includes health promotion and illness prevention. The goal of precision health is to provide people the tools they need to take charge of their health and make wise choices about their food, exercise routine, and other lifestyle aspects.

Precision health is powerful because it can better anticipate, prevent, cure, and control diseases in populations as a whole, not just in individuals. Proactively ensuring a healthy future is just as important as responding to health problems as it is to act reactively.

In order to create healthier communities, precision health is a team endeavour rather than a solo endeavour. A big part of this is the work that public health programmes, often called “precision public health,” do. By emphasising prevention above only treatment, these programmes seek to improve the health of whole communities.

Precision health and medicine hold real potential, not just empty promises. It is coming to pass rather quickly. Healthcare is moving towards a more specialised and focused approach thanks to developments in genetic analysis, the availability of personalised health data, and the integration of lifestyle and environmental data. We are about to see a revolution in healthcare as the available resources and expertise keep growing.

In the far future, your physician will be able to determine your exact illness risks and provide therapies that are tailored to your needs. This is the essence of precision medicine—a window into the real personalised healthcare of the future.

People will be able to make decisions about their health in the future depending on their surroundings, lifestyle, and genetic predispositions. For instance, you can lower your chance of developing a certain disorder if your genetic composition suggests that you are susceptible to it, thereby delaying the beginning of the illness.

Precision health and precision medicine are more than simply catchphrases; they signify a change in the healthcare industry towards a more individualised and accurate approach. We are approaching a time where healthcare is not just reactive but also predictive and preventive as these strategies develop and are more thoroughly incorporated into healthcare systems.

Enhancing health outcomes, cutting healthcare expenditures, and raising both individual and community quality of life are just a few of the many possible advantages. Precision medicine and precision health hold the keys to unlocking this potential future in healthcare, which revolves around personalization, prediction, and prevention. It’s a journey towards greater health, one person at a time, and as a team effort for more wholesome communities.

Are you captivated by the boundless opportunities that contemporary technologies present? Can you envision a potential revolution in your business through inventive solutions? If so, we extend an invitation to embark on an expedition of discovery and metamorphosis!

Let’s engage in a transformative collaboration. Get in touch with us at open-innovator@quotients.com

Categories
Applied Innovation

How Artificial Intelligence is to Impact E-Government Services

Categories
Applied Innovation

How Artificial Intelligence is to Impact E-Government Services

E-government services have become a cornerstone of effective governance in today’s digital age. The goal behind e-governance is to use technology to simplify the delivery of government services to citizens and decision-makers while minimising expenses. Technological innovations have revolutionised the way governments work over the years, but they have also presented new obstacles. Governments must adapt and harness the potential of Artificial Intelligence (AI) and the Internet of Things (IoT) to ensure that the advantages of e-government services reach every part of society.

The Internet of Things and Smart Governance

The Internet of Things (IoT) is a paradigm that entails connecting numerous devices and sensors through the internet in order to facilitate data collecting, sharing, and analysis. IoT has applications in a variety of fields, including transportation, healthcare, and public security. It is a critical facilitator of what we call “smart governance.”

Smart governance is an evolution of e-government in which governments attempt to improve citizen engagement, transparency, and connectivity. This transition is primarily reliant on intelligent technology, notably AI, which analyses massive volumes of data, most of which is gathered via IoT devices.

AI and IoT in Action

IoT and AI integration have a lot of potential to advance how governments operate and how their citizens are treated. Real-time data analysis from highway cameras, for instance, enables traffic updates and problem identification, eventually improving traffic management. AI-driven IoT systems in healthcare allow for continuous monitoring of patient data, facilitating remote diagnosis, and anticipating possible health problems. Additionally, by identifying and following possible threats, the network of linked cameras and data sources improves public safety.

Nevertheless, this upbeat environment is not without its difficulties. These include problems with interoperability that result from the various IoT technologies and raise maintenance and sustainability challenges. As IoT applications are vulnerable to cyber attacks and data privacy problems arise when information is acquired without explicit authorization, data security and privacy are of utmost importance. Ecological issues are also raised by the IoT’s environmental sustainability, which is fueled by its energy-intensive data processing. Particularly in situations where AI makes crucial judgements, such in driverless vehicles, ethical quandaries become apparent. Last but not least, when AI is used in crucial applications, like medical robotics, the topic of accountability arises, raising concerns about who is responsible for unfavourable results.

Challenges of IoT and AI for Smart Governance

Several significant obstacles need to be overcome head-on in order to fully realise the potential of IoT and AI in the area of smart governance. Due of the wide range of technologies that make up the Internet of Things, interoperability is a major concern since it can cause issues with sustainability and maintenance. Second, given the vulnerability of IoT applications to cyber attacks and the advent of data privacy concerns when information is acquired without clear authorization, the crucial issues of data security and privacy come to the fore. Additionally, environmental sustainability is a top priority since IoT’s data processing requirements result in higher energy consumption, which needs attention owing to its potential effects on the environment.

Deeply troubling moral quandaries arise from the use of AI in crucial tasks, like autonomous cars, especially when it comes to prioritising decisions in life-or-death circumstances. Last but not least, the incorporation of AI into crucial applications, such as medical robotics, creates difficult issues relating to responsibility, particularly when unfavourable consequences occur. To fully utilise IoT and AI for smart governance, it is essential to address these issues.

A Framework for Smart Government

The creation of a thorough framework is essential to successfully handle these issues and realise the enormous promise of IoT and AI in the area of smart governance. This framework should cover a number of essential components, such as data representation—the act of gathering, structuring, and processing data. To increase citizen involvement and participation, it should also provide seamless connection with social networks. Predictive analysis powered by AI is also included, allowing for more informed and data-driven decision-making processes. The implementation of IoT and AI applications must be governed by precise, strong rules and laws. Finally, it’s crucial to make sure that many stakeholders—including governmental bodies, corporations, academic institutions, and the general public—are actively involved.

Benefits for All

A wide range of stakeholders will profit from the use of AI and IoT in e-government services. Faster access to government services will benefit citizens by streamlining and streamlining their contacts with government institutions. Reduced service delivery costs benefit government organisations directly and can improve resource allocation. Gaining important insights that can spur more developments in the field and support ongoing innovation is vital to researchers. Additionally, educational institutions may use this framework to improve their methods of instruction and provide students the information and skills they need to successfully navigate the rapidly changing world of IoT and AI technologies. In essence, the changes that will be made under this framework would be for the betterment of society.

Conclusion and Future Directions

In summary, the future of e-government services will be greatly influenced by the combination of artificial intelligence and the internet of things. Despite certain difficulties, there are significant advantages for both governments and individuals. Governments must put their efforts into tackling challenges like interoperability, data security, privacy, sustainability, ethics, and accountability if they want to advance.

Future research should focus on implementation methods, domain-specific studies, and solving the practical difficulties associated with implementing IoT and AI in e-government services. By doing this, we can create a model for government in the digital era that is more effective, transparent, and focused on the needs of citizens.

Are you intrigued by the limitless possibilities that modern technologies offer?  Do you see the potential to revolutionize your business through innovative solutions?  If so, we invite you to join us on a journey of exploration and transformation!

Let’s collaborate on transformation. Reach out to us at open-innovator@quotients.com now!

Categories
Applied Innovation

Leveraging AI, ML, CV, and NLP to transform unstructured data into valuable intelligence

Categories
Applied Innovation

Leveraging AI, ML, CV, and NLP to transform unstructured data into valuable intelligence

In today’s digital era, organizations are swimming in a vast ocean of data, with a significant portion of it residing in unstructured documents. These documents, such as emails, contracts, research papers, and customer feedback, hold a wealth of valuable information waiting to be unlocked. However, extracting meaningful insights from this unstructured data has traditionally been a daunting task. Enter the power of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). These transformative technologies are revolutionizing the way businesses derive value from the data encapsulated within unstructured documents.

Unstructured documents differ from structured data sources, such as databases or spreadsheets, as they lack a predefined format or organized data model. They contain free-form text, images, tables, and diverse information types, making them challenging to analyze using conventional methods. However, advancements in AI, ML, and NLP have paved the way for extracting valuable insights, patterns, and knowledge from these untapped resources.

By applying intelligent algorithms and techniques, businesses can gain a competitive edge, drive innovation, and make informed decisions based on comprehensive data analysis. NLP techniques enable the classification of unstructured text data, such as categorizing emails, research papers, or customer reviews, leading to automated organization and efficient data retrieval. ML algorithms, both supervised and unsupervised, can be used to recognize patterns, detect anomalies, and make predictions within unstructured documents. By employing computer vision algorithms, organizations can automatically classify images, identify objects, and generate textual descriptions, revolutionizing fields like healthcare, security, and manufacturing.

Deriving value from unstructured data is a significant challenge, but leveraging Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV) technologies can help unlock its potential. Here’s a high-level overview of how these technologies can be used:

Data Preprocessing: Before applying AI and ML algorithms, unstructured data needs to be processed and structured. This involves tasks like data cleaning, normalization, and transforming the data into a suitable format for analysis.

Natural Language Processing (NLP): NLP techniques can be used to classify unstructured text data into predefined categories or topics. This can enable automated categorization and organization of large amounts of textual information. Then by Named Entity Recognition (NER), algorithms can identify and extract entities like names, locations, organizations, and other relevant information from unstructured text. AI models then analyze text sentiment to determine whether it’s positive, negative, or neutral. This can be useful for understanding customer feedback, social media sentiment, or market trends. NLP techniques can also automatically generate summaries of large documents or text datasets, enabling quick extraction of key information.

Machine Learning (ML): ML algorithms can be trained on labeled data to recognize patterns and make predictions. For example, ML models can learn to classify images, identify objects, or recognize patterns in unstructured data. Through unsupervised learning, these algorithms can identify hidden patterns or clusters in unstructured data without any predefined labels. This can help in data exploration, segmentation, or anomaly detection. ML algorithms can also analyze user behavior, preferences, and unstructured data such as product reviews or browsing history to make personalized recommendations. Along with things, ML models can learn patterns from normal data and identify outliers or anomalies in unstructured data, which is particularly useful for fraud detection or cybersecurity.

Computer Vision (CV): CV techniques can classify and categorize images or videos based on their content, enabling automated analysis and organization of visual data. These algorithms can identify and locate specific objects within photos or videos. This can be useful in various applications, such as self-driving cars or surveillance systems. Such AI models can also generate textual descriptions or captions for images, enabling better understanding and indexing of visual data.

Use Cases

By combining these technologies, organizations can extract valuable insights, automate manual processes, improve decision-making, enhance customer experiences, and gain a competitive edge by making the most of unstructured data.These technologies can be used to analyze customer feedback from social media posts, reviews, or customer support interactions to understand the sentiment, identify emerging trends, and improve products or services. it can help organizations to automatically categorize customer queries or complaints to prioritize and route them to the appropriate departments for faster resolution. These algorithms can mine unstructured data from customer surveys or feedback forms to extract actionable insights and identify areas for improvement.

Analyzing unstructured data, such as transaction logs, emails, or support tickets, can help identify patterns indicative of fraudulent activities or cybersecurity threats. By applying NLP techniques it can be used to detect suspicious text patterns or anomalies in financial reports, insurance claims, or legal documents. By combining unstructured data sources like social media posts, news articles, and public records to assess reputation or compliance risks associated with individuals or organizations.

Using CV algorithms for facial recognition and object detection in surveillance videos to enhance security measures and identify potential threats or suspicious activities. Analyzing images from medical scans or remote sensing data can be used to assist in diagnosis, detect anomalies, or monitor environmental changes. ML and CV techniques can also be applied to monitor manufacturing processes, detect defects in products or equipment, and ensure quality control.

Extracting structured data from unstructured documents like invoices, contracts, or financial reports to automate data entry, streamline workflows, and improve operational efficiency. Automatically generating summaries or key insights from lengthy reports, research papers, or legal documents to aid in information retrieval and decision-making.

These use cases highlight the diverse applications of AI, ML, NLP, and CV in deriving value from unstructured data across various industries, including finance, healthcare, retail, manufacturing, and more. By harnessing the power of these technologies, organizations can unlock valuable insights, drive innovation, and gain a competitive edge in today’s data-driven landscape.

If you’re interested in exploring these technologies and their use cases further, don’t hesitate to reach out to us at open-innovator@quotients.com. We are here to assist you and provide additional information.

\

Categories
Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

Categories
Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

Every area of healthcare is being significantly impacted by artificial intelligence (AI), and dermatology is no exception. Melanoma identification using AI is one possible application for AI in dermatology. Melanoma is the deadliest type of skin cancer and is difficult to detect and can be fatal. Artificial intelligence (AI) in this context can identify melanoma with a high degree of precision. This is crucial because the number of skin biopsies is increasing while the number of pathologists is decreasing leading to slows down in the rate of identification and, consequently, therapy.

The Process

The process includes the use of Deep Learning to build Convolutional Neural Networks (CNNs), a subcategory of machine learning. CNNs are a form of network architecture for deep learning algorithms and are specifically used for image recognition and other tasks requiring the processing of pixel data. They are therefore perfect for positions requiring computer vision (CV) skills as well as situations requiring precise object detection.

Data collection is the first step in dermatology scans for melanoma, where a sizable dataset of pictures of moles, lesions, and other skin anomalies is gathered and annotated by doctors to build a training set. The machine learning programs’ training on this information comes next during which, the system learns to recognize the characteristics of a melanoma lesion and distinguish them from other kinds of skin anomalies.

After the system is trained it is then incorporated into a dermatologist’s workflow. The dermatologist would capture photos of any suspicious lesions during a skin examination and upload them to the AI system, which would then evaluate the pictures and offer a diagnosis. A possible melanoma lesion would be flagged by the algorithm, prompting the physician to conduct additional testing.

After reviewing the image and the AI-generated analysis, a dermatologist may use additional diagnostic techniques like biopsy to support or contradict the prognosis. In order to increase the precision of the system, dermatologist comments on how well the AI system performed is integrated back into the training data.

An artificial intelligence (AI) system hence helps medical workers in developing possibly successful treatments and improving patient results. It can also increase access to treatment and raise the number of patients who can be seen and diagnosed quickly.

Conclusion

Dermatologists are now outperformed by artificial intelligence (AI) in the diagnosis of skin cancer, but dermatology is still lagging behind radiology in its widespread acceptance. Applications for AI are becoming easier to create and use.

Complex use cases, however, might still necessitate specialist knowledge for implementation and design. In dermatology, AI has a wide range of uses including basic study, diagnosis, treatments, and cosmetic dermatology.

The main obstacles preventing the acceptance of AI are the absence of picture standardization and privacy issues. Dermatologists are crucial to the standardization of data collection, the curation of data for machine learning, the clinical validation of AI solutions, and eventually the adoption of this paradigm change that is transforming our practice.

We want to make innovation accessible from a functional standpoint and encourage your remarks. If you have inquiries about evolving use cases across various domains or want to share your views email us at open-innovator@quotients.com

Categories
Applied Innovation

Artificial intelligence (AI)- the next stage in the transition from conventional to creative farming

Categories
Applied Innovation

Artificial intelligence (AI)- the next stage in the transition from conventional to creative farming

Artificial intelligence (AI) has the ability to transform the way we think about agriculture by bringing` about numerous advantages and allowing farmers to produce more with less work.

With increasing urbanization with the world’s population and shifting consumption patterns, and rising disposable money, Farmering Industry is under a lot of strain to satisfy the rising demand and needs to find a method to boost output. There is a need to search for methods to lessen or at the very least control the risks faced by farmers. One of the most interesting possibilities is the application of artificial intelligence in agribusiness.

Artificial intelligence (AI) is the next stage in the transition from conventional to creative farming. Here we are discussing some applications of AI in agriculture:

Soil and Crop Monitoring

The amount and quality of the yield, as well as the health of the product, are directly influenced by the micro- and macronutrients in the soil.

In the past, personal sight and opinion were used to assess the health of the soil and the crops. However, this approach is neither precise nor prompt and in its place, UAVs can now be used to collect aerial picture data, which can then be fed into computer vision models for intelligent agricultural and soil condition tracking. This data can be analyzed and interpreted by AI much more quickly than by humans in order to monitor agricultural health, forecast yields accurately, and identify crop malnutrition.

Farmers typically have to collect soil samples from the ground and transport them to a facility for labor- and energy-intensive analysis. Instead, researchers chose to investigate whether they could teach a program to perform the same task using image data from a low-cost handheld camera.

The computer vision model was able to produce approximations of sand composition and SOM that were as accurate as pricey lab processing.

Therefore, not only can computer vision remove a significant portion of the labor-intensive, manual work involved in crop and soil track, it often does so more efficiently than people.

Monitoring Crop Maturation

To maximize output effectiveness, it is also crucial to watch the growth stages. To make changes for better agricultural health, it’s essential to comprehend crop development and how the climate interact.

Precision agriculture can benefit from AI’s assistance with labor-intensive processes like manual development stage monitoring. For producers, overserving and overestimating agricultural development and maturity is a difficult, labor-intensive task. But a lot of that labor is now being handled with ease and remarkable precision by AI.

The farmers no longer needed to make daily trips out into the fields to inspect their crops because computer vision models can more correctly spot development phases than human observation. Computer vision can determine when a crop is mature by using an algorithm that examined the hue of five distinct crop components, estimated the crop’s maturity, and then used this information.

Detecting Insect and Plant Diseases

Plant pest and disease monitoring can be mechanized using deep learning-based picture recognition technology. This works by creating models of plant health using picture categorization, detection, and segmentation techniques. This is accomplished by using pictures of rotten or diseased crops that had been labeled by botanists according to four main phases of intensity to training a Deep Convolutional Neural Network. The substitute for machine vision entails extensive, time-consuming human searching and review.

Livestock Monitoring

Farmers can keep an eye on their livestock in real time by using AI. Dairy farms can now separately watch the behavioral characteristics of their cattle thanks to artificial intelligence (AI) solutions like image classification with body condition scores, feeding habits, and face recognition. Additionally, farmers can keep track of the food and water consumption as well as the body temperature and behavior of their animals. These benefits of AI are the main reasons the farming industry is seeing a sharp rise in demand for it.

Conclusion

Technology has been employed in farmland for a very long time to increase productivity and lessen the amount of demanding manual work needed for farming. Since the advent of farming, humankind, and agriculture have evolved together, from better plows to drainage, vehicles to contemporary AI.

Computer vision’s expanding and more accessible supply could represent a major advancement in this area. Because of the significant changes in our climate, environment, and dietary requirements, AI has the potential to revolutionize 21st-century agriculture by boosting productivity in terms of time, labor, and resources while also enhancing environmental sustainability. By implementing real-time tracking to encourage improved product quality and health, it is also enhancing agriculture.

Categories
Global News of Significance

UK government funding research on artificial intelligence (AI) to advance healthcare

Categories
Global News of Significance

UK government funding research on artificial intelligence (AI) to advance healthcare

The UK government has committed almost £16 million to cutting-edge study in artificial intelligence (AI).

The third round of the AI in Health and Care Awards has awarded funding to nine businesses, accelerating the testing and application of the most cutting-edge AI technologies. The awards were established in 2019 to advance AI technology aimed at assisting patients in managing chronic diseases and enhancing the speed and precision of diagnostics.

The victors include AI systems that can assist the treatment of neurological disorders like dementia, spot cancer, identify women at the greatest risk of preterm delivery, and diagnosis uncommon illnesses. The money will be used to assist the National Health Service in testing, reviewing, and adoption of these companies’ innovations.

One of the companies performs breast cancer screenings using an AI-driven program. By analyzing pictures of tissue samples, the technology enables doctors to identify cancer more rapidly. Another winner in the medical device industry, has been releasing gadgets and treatments to combat more than 30 chronic illnesses, such as diabetes and Parkinsons. A digital health start-up that supports an AI system that analyses electronic health data to identify patients with unidentified uncommon illnesses and suggest the best management strategies has also received an award. A consortium headed by a university has also been awarded that uses an online medical tool to identify pregnant women who are most at risk of giving birth early or experiencing problems that could result in birth defects.

One of the top 5 objectives of the UK government is reducing wait times for the National Health Service, which is supported by record spending of up to £14.1 billion for health and social care over the next two years.

The government is confident that technological advancements, such as those in robotics and artificial intelligence, will give people more control and aid in the fight against some of the largest healthcare challenges, such as genetic illnesses and cancer. Innovations of this nature can expedite diagnostics and therapies while freeing up staff time.

Source: Gov.uk

Categories
Applied Innovation Industry 4.0

How AI is impacting the textile industry

Categories
Applied Innovation Industry 4.0

How AI is impacting the textile industry

The textile industry is looking for high-quality low-cost strategies to differentiate itself and take production ROI into consideration due to growing competition led by high labor expenses.

Problems with the manual approach

Most of the time, in the textile industry, inspecting yarn is done manually, which takes a lot of time and work. Operators stationed at various lines do the check, they have to pick up a variety of goods at random and examine them with their unaided eyes. They determine the fibers’ grade by visual inspection and separate them accordingly. Due to this manual approach wide variety of faults, including stains, deformation, knots, broken yarn, splitting, fuzzy edges, and incorrect color, missed inspections are also rather prevalent. Rule-based vision systems are prone to high rates of incorrect detections and require manual double-checking when errors are irregular or occur in large numbers. Yarn inspection requires a more dependable approach in order to increase labor productivity.

AI-powered solution

Using an AI-powered solution several kinds of yarn flaws may be detected and identified through picture analysis. The AI model can swiftly and precisely find faults to increase detection rates and manufacturing output while lightening the load on manual inspection. The technology can continue to refine the AI recognition process as the amount of accessible data grows and enable the speedy transfer of training results into multiple manufacturing lines.

This involves the use of an appearance tester that tests the samples of yarn taken from the lab. Vectors are defined and utilized as network inputs, and sample photos are taken and preprocessed. Then feed-forward neural networks are employed that had been trained using the back-propagation rule. Better outcomes may be achieved by using a multilayer neural network in conjunction with picture enhancement to estimate various yarn metrics. As a result, a modeling system may be effectively constructed.

Artificial intelligence (AI) can also be used in forecasting yarn properties and dye recipes. By analyzing past data on dye recipes and their outcomes and extrapolating this understanding to the characteristics of new dye recipes, AI may also be used to predict the quality of dye recipes. Machine learning algorithms or other types of AI techniques may be used to achieve this. To identify trends and forecast results, one method is to employ machine learning algorithms, which can be trained on vast datasets of yarns, textiles, and dye recipes. A machine learning model, for example, may be trained on a dataset of fibers with known attributes, such as strength and fineness, in order to predict the characteristics of new fibers. Similar to this, a machine learning model that has been trained on a dataset of well-known yarn attributes like tensile strength and elongation may be used to predict the properties of new yarns.

The advantages of implementing a machine vision system include an inspection accuracy of about 98 percent and each product may be inspected. Overall, the use of AI for yarn property prediction, fiber grading, and dye recipe prediction may assist manufacturers in improving the quality and effectiveness of their processes, resulting in significant cost savings and improvements in product performance.

Our innovators have developed solutions for computer vision for inspection and forecasting yarn properties and dye recipes. Some of these solutions have been successfully implemented at different levels. Please write to us at open-innovator@quotients.com to know more about these solutions.

Categories
Applied Innovation

How AI can impact Maritime Logistics?

Categories
Applied Innovation

How AI can impact Maritime Logistics?

Investing in communication technologies has provided several benefits for shipping firms. Most ships have grown into remote offices at sea, providing the captain and crew with dependable Internet connectivity, virtual networks, email, route planners, and a variety of other technologies and applications. Further investing in innovative technologies can enhance regular vessel operations while also lowering corporate expenses and optimizing business processes.

Machine Learning enables users to use sophisticated algorithms and analyze data, which aids in guiding the logic of potential issues in marine transportation. These approaches may be used for maritime network design, trip planning, cargo optimization, maintenance processes, and other areas.

Machine learning, a branch of Artificial Intelligence, relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances. It enables the use of intelligent algorithms and the evaluation of data, which aids in guiding the logic of potential issues in marine transport. These algorithms may be applied to maritime network design, trip planning, cargo optimization, and other applications.

The intelligence of ML algorithms, combined with industry knowledge, has the potential to provide a significant advantage to shipping companies that first adopt them in their operations. The bigger the investment in AI/ML, the more advantage from their big data analysis capabilities as ML algorithms can handle data from the whole history of a vessel’s operation.

Advanced Machine Learning algorithms will be capable of enhancing trip optimization, such as fuel economy, crew performance, voyage cost estimates, calculating the ideal route in a minute, and providing advice on speed, course, and so on. ML algorithms, for example, may be used to estimate fuel usage based on engine data and vessel parameters. These algorithms enable the transformation of massive amounts of noisy sensor data and other onshore data into organized information that may be used to anticipate fuel usage and map ideal paths for boats.

As data is a critical component for removing uncertainty, adopting ML algorithms can assist to boost the usual data that might be critical for shipowners. Data mining in the marine industry has been quite restricted thus far. As a result, as compared to other industries, the deployment of ML approaches in marine transport is restricted. Taking this into consideration, our innovators have created solutions incorporating edge platforms, machine learning models, onboard sensors, and application software. We have solutions for Predictive Scheduling, Container Positioning Organization, Voyage Planning and Route Forecasting, Fuel Consumption Optimization, and Predictive Maintenance.

We would be pleased to hear from you and would want to discuss potential partnership opportunities. Please write to us at open-innovator@quotients.com


Categories
Applied Innovation Healthtech

Federated Learning for Medical Research

Categories
Applied Innovation Healthtech

Federated Learning for Medical Research

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) have emerged as the most popular and fascinating technologies in the intelligent healthcare industry.

The traditional healthcare system is centered on centralized agents providing raw data. As a result, this system still has significant risks and problems. When combined with AI, the system would consist of several agent collaborators capable of successfully connecting with their intended host.

Federated Learning, a novel distributed interactive AI paradigm, holds promise for smart healthcare since it allows several clients (such as hospitals) to engage in AI training while ensuring data privacy. FL’s noteworthy characteristic is that operates decentralized; it maintains communication based on a model in the selected system without exchanging raw data.

The combination of FL, AI, and XAI approaches has the potential to reduce the number of restrictions and issues in the healthcare system. As a consequence, the use of FL in smart healthcare might speed up medical research using AI while maintaining privacy.

The Federated Learning approach may be used to provide several enticing benefits in the development of smart healthcare. Local data, for example, are not necessary for training. To train other machine learning algorithms by mixing a large number of local datasets without transmitting data. During training, local Machine Learning (ML) models are trained on local heterogeneous datasets.

When opposed to traditional centralized learning, FL is also capable of delivering a good balance of precision and utility, as well as privacy enhancement. FL may also help to reduce communication costs, such as data latency and power transmission, connected with raw data transfer by avoiding the dumping of huge data quantities to the server.

We have solutions that use FL to link life science enterprises with world-class university academics and hospitals in order to exchange deep medical insights for drug discovery and development. The platform enables its partners to uncover siloed datasets while maintaining patient privacy and securing proprietary data by leveraging federated learning and cutting-edge collaborative AI technologies. This enables unprecedented cooperation to enhance patient outcomes by sharing high-value knowledge.

The platform has built a worldwide research network driven by federated learning, allowing data scientists to securely connect to decentralized, multi-party data sets and train AI models without the need for data pooling. When combined with fields of medicine specializing in diagnosis and treatment, scientists may use cutting-edge technology platforms to build potentially life-changing drugs for people all over the world.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

Categories
Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

Categories
Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

Incredible medical discoveries are revolutionizing our ability to treat and even cure patients; but, drug discovery and development is becoming more difficult and costly, leaving many patients without viable medicines.

Simultaneously, throughout the last decade, a revolution in machine learning has enabled answers to issues that were formerly deemed intractable. Machine learning approaches can currently caption photos, translate across languages, and identify voices at or above human performance levels.

One of the systems utilized in AI is neural networks, which may be used to identify chemical structures with medicinal significance. A neural network uses a large set of training data containing information about the chemical structure-biological activity relationship, which is preceded by successful neural network training and acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity.

The data is derived through experimental observations as well as from relevant quantum models. There were constraints in biological data a few years ago – while access to huge, rich data sets has spurred machine learning’s development, such data sets are still rare in biology, where data collection remains essentially artisanal. Recent advances in cell biology and bioengineering are now allowing us to change this by facilitating the generation of huge volumes of biological data. Besides, researchers have revealed that neural networks have a substantial capacity to create generalizations based on even very restricted training data.

Pipelines for drug discovery and development are lengthy, complicated, and dependent on a variety of factors. Machine learning (ML) techniques offer a collection of tools that can enhance discovery and decision-making for well-specified queries with a large amount of high-quality data. Opportunities to use ML arise at various phases of drug development.

Instead of depending on restricted “discovered” data, we have solutions that use contemporary biology technologies to build high-quality, huge data sets designed for machine learning, allowing us to unleash the full power of modern computational methodologies.

Our solutions are created by professional biologists and drug hunters who collaborate with cutting-edge technologists and machine learners. A group of life scientists and data scientists, software engineers, process engineers, bioengineers, translational scientists, and drug hunters are collaborating to answer problems that we would never have thought to ask on our own.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com