Category: Applied Innovation

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Applied Innovation Industry 4.0 Uncategorized

How XR Technology is transforming the Maintenance and Repair Industry

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Applied Innovation Industry 4.0 Uncategorized

How XR Technology is transforming the Maintenance and Repair Industry

Everyone is raving about the new, intriguing technology known as XR. But what does XR actually imply, and when can this technology be used? Cross reality, also known as expanded reality, is a general word for several distinct but connected technologies, which include Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR).

XR, MR, VR, and AR

The terms XR, MR, VR, and AR are frequently used synonymously but there is a definite distinction between these. Virtual Reality is a type of XR that uses a head-mounted display (HMD) or smartphone, or other gadgets placed about an inch away from the viewer’s eyes to fully immerse the viewer in a simulated world. Here the display of the gadget completely fills the user’s field of view. However, not all XR is virtual reality (VR).

Similarly, Augmented Reality is a subset of XR that uses digital enhancements to improve the user’s perception of the actual world. Typically, a device’s camera is used for this, which records the real-world scene and superimposes digital components on top of it. For instance, augmented reality (AR) may phone’s camera to project game figures onto the screen of a smartphone, giving the impression that they are in the same room as you.

MR, or “mixed reality,” is a fusion of VR and AR. In other words, Virtual reality and augmented reality are combined to create Mixed Reality. Users can engage with both physical and digital objects thanks to MR’s anchoring of digital components to the real world. Usually, specialized gear, like Microsoft’s HoloLens, is used to accomplish this. Overall, it’s critical to comprehend the distinctions between XR technologies because each has particular advantages and disadvantages and is best suitable for particular uses.

Application of XR in Different Industries

XR technology by combining the physical and virtual worlds improves safety, efficiency, and productivity while reducing costs and downtime. It has the potential to revolutionize the way we work and is impacting many industries like Gaming and Entertainment, Employee Training, Customer Support, Healthcare, Property and Real Estate, and Retail.

Application of XR in Maintenance and repair


Maintenance and repair work is one of the most important applications where XR is having an impact. Technicians can obtain real-time information, such as directions, blueprints, and troubleshooting manuals, about the equipment they are working on by using AR overlays. This technology may increase the precision and effectiveness of maintenance and repair work, decrease delay, and eventually result in cost savings for companies.

Traditionally, techs have user manuals and blueprints to identify and fix equipment. This approach, though, can be laborious and error-prone. On the other hand, XR technology gives workers a visual depiction of the equipment, enabling them to swiftly recognize the issue and fix it. This is especially advantageous for sophisticated apparatus and equipment, where even a small error can result in expensive downtime and maintenance costs.

Additionally, step-by-step directions on how to fix or keep the equipment can be provided using AR overlays. This benefits technicians who may be inexperienced with a specific sort of machinery or lack the required knowledge. Technicians can more swiftly and correctly identify and fix machinery with the help of real-time guidance.

Repair workers as well can be trained using XR technology on how to operate and manage equipment. The use of VR models can give techs access to a regulated and secure virtual world where they can practice maintenance and repair tasks. This can lower the possibility of mishaps and equipment harm while training, as well as increase the techs’ effectiveness and accuracy in real-world situations.

Remote assistance can also be provided using AR graphics. With the aid of smart eyewear, techs can communicate in real-time with specialists who can assist them virtually throughout the maintenance or repair process. When working with complicated equipment that calls for specialized knowledge and experience, this can be especially helpful.

In general, XR technology has the power to completely transform the servicing and repair sector. Businesses can increase the precision and effectiveness of maintenance and repair tasks, decreasing delay and eventually saving money, by giving workers real-time information, step-by-step directions, and online assistance. It will be fascinating to observe how XR technology develops in the future and how it will help companies run more effectively and efficiently.

Please write to us at open-innovator@quotients.com to learn more about such innovative solutions and partnership opportunities.

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Applied Innovation

Expanding role of Geoanalytics in Retail Industry

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Expanding role of Geoanalytics in Retail Industry

Geospatial data and technology can provide the retail industry with useful insights to help understand consumer behavior, optimize store locations, and improve marketing efforts. It can play an important role in every stage of retail business, from site selection to customer delivery as each of these steps requires a spatial component to plan activities and make decisions.

With the emergence of studies on customer purchasing behavior geoanalytics is playing a critical role and companies are working to redefine their supply chain strategy and rethink their business processes and customer loyalty strategies. GIS data is extensively being used to discover new business opportunities and improve productivity.

Geoanalytics can help businesses to Identify areas of high customer demand by analyzing data on customer behavior and demographics. It can also aid in optimizing store locations and formats and identifying the best locations for new stores. By analyzing customer data and behavior, geoanalytics retailers can tailor their marketing and promotions to specific customer segments and geographic areas. Geospatial analytics can also help in optimizing inventory management by identifying which products are most popular in which areas and predicting demand based on customer behavior.

Some areas in which geoanalytics is playing its role in the retail industry is being discussed below:

Site selection: Retailers can learn a lot about the racial composition and spending habits of prospective consumers in a given region by analyzing geospatial data. With the help of this data, they can decide where new stores would be most successful, taking into consideration things like customer behavior, wage levels, and population density. Making data-driven choices about where to locate new shops can assist retailers in increasing sales and profitability.

Store plan: By examining consumer behavior, such as how they move around the store, where they spend the most time, and which goods they are most likely to buy, retailers can use geospatial data to optimize the plan of their stores. With the aid of this information, merchants can create shop designs that direct consumers to particular goods and boost sales. Retailers can enhance the shopping experience for customers and boost revenue by optimizing the arrangement of their stores.

Marketing: Retailers can also use geographic data to develop specialized marketing strategies that will connect with their target market more effectively. Retailers can develop marketing strategies that are tailored to particular geographic regions, demographics, or customer categories by analyzing data on customer purchasing patterns and tastes. Merchants may benefit from this by increasing the ROI of their marketing investments and the efficiency of their marketing efforts.

Inventory Management: Retailers can streamline their inventory management procedures by analyzing geospatial data on product demand, operations in the supply chain, and store sites. They can use this information to determine which goods are popular where and how much merchandise they should keep on hand. Dealers may be able to lower holding costs for goods and increase supply chain effectiveness as a result.

Competition Analysis: Merchants can use geospatial data to analyze their competing environment and learn more about the consumer base, pricing policies, and marketing strategies of their rivals. Retailers can use this knowledge to inform their pricing and marketing choices so they can compete effectively in their market.

In conclusion, geoanalytics can be a potent instrument for retailers to gain a competitive edge by comprehending consumer behavior, optimizing store locations, and enhancing marketing initiatives. Retailers can use geospatial data to make data-driven choices that could result in higher sales, profits, and consumer satisfaction.

Are you interested in implementing geoanalytics in your company? Quotients through its partner networks offers quicker, and more affordable alternatives without the constraints of time, money, and resources. Please write to us at open-innovator@quotients.com to know more about these solutions.

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Applied Innovation

AI can analyze data from news reports and social media posts to predict disease outbreaks

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AI can analyze data from news reports and social media posts to predict disease outbreaks

AI can serve as an effective tool that could be extensively used in clinical and public health decision-making in order to successfully manage a pandemic. AI and machine learning can be used to anticipate and react to disease outbreaks like creating early detection systems capable of detecting and tracking illness outbreaks in real time.

Policymakers and governments have a wide range of options for population-level health initiatives, which are essential for early-stage disease management. There are various non-pharmaceutical interventions available that can help in containing the rise of a pandemic. These include travel restrictions, company closures, school closures, mask mandates, and distribution of scarce resources such as personal protective equipment (PPE) and testing. Many of these choices rely on expert advice rather than data-driven algorithms but this has been changing post-COVID-19.

Data has always been essential in healthcare and public health decision-making; however, data proved to be particularly useful in global efforts to combat COVID-19. Unprecedented levels of worldwide cooperation have sparked data-sharing efforts from both traditional and non-traditional sources. The data generated in form of social media posts and news reports are also available for analysis that can be used to come to a conclusion and figure out the response by the government and other bodies.

An AI-powered platform can monitor and collect data from various sources, such as news reports, social media, and government notifications.  It can analyze this data using machine-learning techniques to detect possible disease outbreaks. An early warning device can notify public health authorities in real time of outbreaks, enabling them to react swiftly and contain disease spread.

Diseases are sociobiological phenomena that leave both social and microbiological traces, and using both AI and public data, such as social media posts, may aid in monitoring human society for indications of odd activity that may indicate the rise of new pathogens with pandemic potential.

By examining social media posts and other data in the months preceding the epidemic can be seen if there were any patterns or trends that could have given an early warning of the virus. Using this technology in a pandemic-focused early detection method could allow for faster reactions in public health, medicine, and government.

The system analyzes social media posts for early indications of disease epidemics and rising health issues using natural language processing (NLP) and machine learning algorithms. The aim is to detect possible outbreaks before they proliferate and to take preventive measures. Predictive models for AI-based tools and apps are presently being developed and evaluated.

There are also obstacles that have to be overcome such as data privacy and bias, as well as guarantee that the data gathered is accurate and reliable. There are concerns about privacy, data security, and the potential for prejudice in automated decision-making. Overall, it can be stated that AI has great potential to improve health care and predict outbreaks and hence improving responses, but cautious attention must be given to its application and ongoing tracking to ensure that it is used effectively and ethically.

Please write to us at open-innovator@quotients.com to know more about such innovative solutions and partnership opportunities,

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Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

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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

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Applied Innovation

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

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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.

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Applied Innovation

How Geoanalytics is helping solve Complex Problems for Businesses

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How Geoanalytics is helping solve Complex Problems for Businesses

Today, almost every company is using Geoanalytics to make better choices, whether for handling store locations, customizing marketing and sales strategies by area, maximizing foreign operations, responding to location-specific trends, improving shipping and movement of products, or any other related problems. Organizations can better comprehend complex relationships in data and analyze them by using analytics techniques that utilize geographic data.

Geoanalytics uses location-based information like locations, zip codes, GPS positions, and more in analyses for contextual knowledge and various views on the data being examined. Incorporating geo-location and other spatial information, can help businesses in obtaining a better grasp of their data and discover new insights. Geoanalytics thus provides more detailed, full perspectives of the data and allows comparisons across cities, regions, and countries, as well as to spot trends and patterns.

Benefits of Geoanalytics

There are several advantages of using geographic data. For example, Geospatial data, through data abnormalities, can alert companies to impending changes that will impact their business. It can also help companies understand why and how some areas are working well while others are not. It can also improve the effectiveness and operational efficiency of enterprises by using precise accuracy given by geospatial data.

Businesses can use geoanalytics in data visualisations to create a exact picture of what is going on and identify patterns more effectively. Heat maps and density charts, in particular, can be helpful in understanding spatial data distributions. Powerful map representations and location-based analytics can reveal vital geospatial information and uncover hidden geographic connections, resulting in improved location-related choices.

Geoanalytics benefits more than just companies and can be used by government organizations, nonprofits, and community service providers as well to find areas in need, at-risk groups, and gaps in access to care.

Use Cases of Geoanalytics

Let’s now look at some of the use cases of geoanayltics

GIS Data for Effective Physical Store Sites

Companies, understandably, battle when deciding where to locate their shops or depots. It should be carefully planned, striking a compromise between procurement suitability and improved client reach while keeping costs in mind.

Companies can use location intelligence to blend sales data with client geographical spread to determine the best location for opening up a business. Retailers have been trying and developing further to determine the regional tastes of their target group in order to drive successful retail strategies, such as finding busy hours and managing parking spaces and store employees appropriately. Furthermore, they can distinguish between lucrative and unprofitable shops using this data.

Crop Production Prediction Using Satellite Imagery

A good or poor harvest can have a knock-on impact on supply, production, and demand, throwing your running expenses into disarray. Geoanalytics is widely used by developed and emerging businesses to predict staple crop output. Satellites can determine the condition of vegetation based on its hue. This data can be converted into agricultural yield metrics using statistics and modeling methods.

Improvement of Supply and Transportation Routes

Supply and delivery firms use satellite images for route optimization to reduce transportation costs, reduce deadhead and empty kilometers, and optimize unit economies. Distribution costs can be reduced even further if the warehouse is properly situated.

The business is under enormous pressure to provide the finest possible experience to customers. Modern route optimization methods employ spatial data science, which takes into account not only storage locations but also fulfillment center locations and capabilities.

Data models can be created using the spatial analytics-based method to mimic network circumstances based on fundamental limitations. The successful implementation of such efforts leads to improved visualization of route performance, and thus in the formulation of optimum transportation plans, and the elimination of bottlenecks.

Checking the Validity of Insurance Claims Using Locational Data

The most difficult issue for insurance firms is sorting out legitimate claims from fraudulent ones. Insurance data intelligence on their clients’ information is one of the ways these businesses fight such threats. One of these is a study of spatial data to determine their clients’ risk exposure based on where they live.

Insurance companies use GIS data to track down non-credible claims, which allows them to focus their efforts on clients in desperate need and expedite claim handling. Some locations or places are more vulnerable to natural catastrophes or criminality, resulting in a larger number of claims. Customers’ locational info can be used by insurers to charge greater premiums.

Better Shared Infrastructure for All

Geoanalytics can be a significant move towards bettering citizens’ livelihoods and can be used to enhance a variety of public utilities. It can assist in carefully placed public amenities such as hospitals, schools, and police offices, which can lead to increased foot traffic and improved accessibility for the people.

The route optimization techniques discussed for logistics and warehouses can be used by government services that require transportation such as post offices, freights carry goods, ambulances, garbage collection, etc. to find the best available routes and reduce resource wastage in terms of fuels, time, wear and tear, or perishability.

Optimization of Piping Layout

Pipelines are the most efficient way to move fuels, LPG, or water sources; however, the starting cost is prohibitively high, necessitating optimization at every step of the way. Locational intelligence can be used in pipeline least-cost route analysis. As we all know, the quickest route between two locations is a straight line, but existing services and infrastructure, as well as topography (uneven terrain), make this impossible—the least-cost path analysis considers all of these characteristics, as well as environmental factors and help businesses coming to the right answer.

Are you interested in implementing geoanalytics in your company? Quotients through its partner networks offers quicker, and more affordable alternatives without the constraints of time, money, and resources. Please write to us at open-innovator@quotients.com to know more about these solutions.

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Applied Innovation Healthtech

How Healthcare Can Benefit From Data Lakes

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How Healthcare Can Benefit From Data Lakes

Healthcare companies are racing against the clock to improve the efficiency of their electronic health data (EHR). As a consequence, healthcare providers need to create new analytical models in order to spot at-risk patients, avoid adverse events, and practice evidence-based medicine. The emergence of cutting-edge research and forecasting models has led healthcare organizations to use data to verify theories and improve them using these techniques.

Data-informed Healthcare Organisations

The boom in AI and machine learning capabilities, and the accessibility of high-performance, storage-efficient hardware via the Commercial Cloud have all contributed to the emergence of the data-informed healthcare organization. The entrance hurdle for data-informed intelligence has decreased as a result of the market’s recent influx of technology and talent. This has ushered in a decrease in expense and improved the learning curve propelled by market and innovation.

Having a solid data infrastructure is the first step toward becoming a data-informed healthcare company. The information must be safe while still being easily accessible to those who require it. Systems must be able to search through the data in a matter of seconds or less, also the data must be inexpensive to keep in extremely large quantities. Complex data such as JS Object Notation or pictures. must be available through common query languages like SQL.

Enter the Healthcare Data Lake, a compilation of datasets that includes clinical data from electronic health record systems, societal factors of health data, analytical output from quality assessment and risk adjustment programs, and patient claims history.

Understanding Data Lakes

When compared to the clean, processed data kept in conventional data warehouse systems, a data lake’s ad hoc character is indicated by the word “data lake.”

A data lake is a gathering of different data assets that are kept within a Hadoop ecosystem with little alteration to the original format or substance of the source data. It is not just Big Data. As a result, the data lake does not have an explicit schema-on-write. Several programs that use “schema-on-read” are used to access the data lake’s information.

Big data from numerous sources are kept in a raw, granular version in a data lake, which is a primary storage location. Data can be retained in a more flexible shape for future use because it can be stored in an organized, semi-structured, or unstructured manner. A data lake identifies the data it stores with IDs and metadata markers to speed up retrieval.

Leveraging Data Lake for Healthcare

The data lake produces one complete, consolidated source of data for healthcare companies by removing the barriers posed by siloed data sources in various forms. This must be accessible on-demand to aid a motley of clinical and business use cases.

Healthcare organizations and health plans are considering whether their general data design needs a corporate data repository, a data lake, or both.

Some businesses seek to address complicated problems with a combination of alternative and conventional implementations, and there is a market change towards mixed approaches to data management.

The challenge facing healthcare leaders is to drive member and patient involvement, enhance patient medical results, and reduce the cost curve. These organizations must have the capacity to quickly ingest and evaluate sizable quantities of data in batch or in real-time from a wide range of sources in a variety of forms in order to accomplish this.

Benefits of Data Lake for Healthcare

Healthcare companies can enhance their data with data lakes to obtain more complete, useful insights to drive therapeutic and business efforts. For example, it can assist healthcare companies to leverage clinical data in order to find populations or diagnoses that may be under-reported for risk and quality initiatives. Additionally, it can provide access to real-time clinical data to care managers, allowing them to effectively prevent unnecessary emergency room visits, hospitalizations, and so on.

Data lakes can also aid in the integration of useful clinical results into provider report cards, as well as the tracking of opiate prescription trends to spot potential patient safety concerns and discover instances of fraud, waste, and abuse. Benefit design, network, and quality efforts can all gain from evaluating member care-seeking trends using data lakes.

We have innovators working to expedite data-centric techniques which enable biopharma and medical technology firms to provide cost-effective treatments to patients more quickly. Please contact us at open-innovator@quotients.com if you want to learn more about such solutions.

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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

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UK government funding innovative research on artificial intelligence (AI) to advance healthcare
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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

Clinical Data Intelligence for Life Sciences solutions is making data gathering and categorization effective and intelligent, lowering mistakes and speeding up submissions.

In the age of machine learning, artificial intelligence, and semantic data pools, no nugget of information is wasted. The healthcare sector has advanced significantly in clinical decision support and predictive analytics in just the last few years.

As Data are becoming more accessible in the healthcare sector as opposed to a siloed strategy. The use of technology and data and data-driven value creation is now being witnessed throughout the network. Healthcare organizations now have the chance to better leverage data, improve patient care, and increase revenue while handling increasing risks in patient privacy and data security as new data technologies with advanced intelligence capabilities become available.

With businesses investing more in population health management and accountable care, the use cases for big data are multiplying quickly, and consumers are keeping up with the demand for affordable services that take advantage of the ease of their preferred applications and devices. This results in better treatment outcomes, individualized care, and preventive interventions. We would be discussing some of the emerging use cases going forward.

Preventive Healthcare: Preventive Healthcare is one of the use cases for clinical data intelligence. It enables experts to identify dangers early and take preventative measures. Through the use of data science techniques like AI and machine learning, wearables and other tracking devices that gather and track data are producing forecast models that can correctly identify a person’s health risks and enable carers to take preemptive action. It is feasible to anticipate and avoid chronic cardiac conditions, autism meltdowns, etc. by utilizing genetic and historical data.

Data-Driven Care: Data science technologies can make uses like medical image analysis and pathology reports that read with high precision possible because a large number of patients perish each year as a result of diagnostic mistakes. To analyze and understand X-rays, MRIs, mammograms, and other imaging studies, as well as to spot trends and identify illnesses, data models and algorithms can be created. This will increase output and aid doctors in making correct diagnoses.

Individualized Care: A one-size-fits-all strategy for medical care and medication is also now considered ineffective. The ability to monitor individual data and improvements in gene technology are enabling customized medicine. Based on a patient’s prior medical history, gene markup, and current data, machine learning, and deep learning algorithms can now help a doctor determine whether a specific drug will be effective for the patient.

Lowering Costs: Insurance firms are putting weight on healthcare organizations to improve therapy outcomes in order to lower readmissions. Longer personal care is a consequence of bed shortages in some nations. By enabling doctors to remotely monitor their patient’s vital signs, receive alerts when conditions deteriorate, and take appropriate action when required, data science and intelligence can significantly assist in resolving these problems.

Drug discovery: Drug development and clinical studies are lengthy, expensive procedures. Data intelligence tools can aid scholars in the analysis of huge data sets and in the creation of computer models for various tests. Additionally, text mining can assist medical academics by automatically reviewing thousands of web resources and performing analytical processing quickly to give the required information. Clinical trials will use data science apps to accelerate findings and reduce costs.

Thus providing companies with cutting-edge capabilities can improve care, accomplish improved treatment outcomes, increase patient experience, and make new discoveries in drug discovery, data science, and intelligence will have a major influence on the future of the healthcare sector.

However, healthcare companies are still having trouble mastering descriptive analytics, particularly when valuable insights call for a variety of data sources. Despite the data-driven promises, the majority of healthcare companies still have a lot of work to do before they can turn their growing big data analytics skills into actually usable clinical information.

Are you interested in implementing data science and intelligence in your company? Quotients through its partner networks offers a quicker, more affordable alternative. Using advanced data science technologies like AI, machine learning, deep learning, etc. without the constraints of time, money, and resources is made possible by our solutions. Please write to us at open-innovator@quotients.com

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Applied Innovation Industry 4.0

How AI is impacting the textile industry

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How AI is impacting the textile industry [Image Courtesy: Pixabay ]
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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.

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Applied Innovation

How Smart Contracts can transform Traditional Financial Services

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Smart Contract in finance [Image Courtesy: Pixabay]
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Applied Innovation

How Smart Contracts can transform Traditional Financial Services

Smart contracts

Smart contracts are essentially programs that run when certain criteria are satisfied and are recorded on a blockchain. They are often used to automate the implementation of an agreement so that all participants may be confident of the conclusion instantly, without the participation of an intermediary or time lost. They can also automate a workflow by automatically activating the next activity when certain circumstances are satisfied.

Smart contracts operate by executing basic “if/then” statements encoded into blockchain code. When preset circumstances are met and validated, a network of computers conducts the activities. These activities might include transferring payments to the proper parties, registering a vehicle, providing alerts, or issuing a ticket. Only those who have been granted permission can see the results.

A smart contract can have as many specifications as necessary to reassure the participants that the work will be executed correctly. Participants must identify how transactions and associated data are represented on the blockchain, agree on the “if/when” rules that govern those transactions, investigate all conceivable exceptions, and design a framework for resolving disputes in order to set the terms.

The smart contract can then be coded by a developer; however, firms that use blockchain for business are increasingly providing templates, web interfaces, and other online tools to facilitate smart contract construction.

Benefits of smart contracts

Smart Contracts are a faster, cheaper, and more secure way of executing and managing agreements. Some other benefits of smart contracts are discussed here:

  • Accountability: The participants know the same information at the same time, which reduces the possibility of contract clause manipulation. Because smart contracts are built on blockchain, they ensure the immutability of data, allowing contracts and agreements to be created without the need for the parties to know each other and preventing potential violations of conditions or mistakes in contract administration and implementation. This openness provides the parties with security and confidence since the data and information relevant to the contract are available to them during the contract’s life cycle, and transactions are copied so that all parties involved have a record.
  • Autonomy: Smart contracts do not require trusted third parties or human participation in the process, allowing the parties autonomy and independence. This intrinsic property of smart contracts provides additional benefits such as cost savings and increased process speed.
  • Cost-cutting: This benefit is also associated with the removal of middlemen. The related expenses are minimised since there is no need to rely on a third party to verify the terms of the contract and offer the required trust. Intermediary costs are eliminated in this sort of contract.
  • Speed: The elimination of intermediaries lowers both the economic and time costs. Because it is done automatically, it takes less time than contracts done manually and in the presence of a third party.
  • Updates performed automatically: Because of its technical and autonomous character, the contract conditions are automatically altered and updated, eliminating not only the need for intermediaries but also the need for new processes to carry out these revisions.

Application of Smart Contracts in Financial services:

Smart contracts contribute to the transformation of traditional financial services in a variety of ways. In the case of insurance claims, they verify for errors, route them, and then send compensation to the user if everything checks up.

Smart contracts include essential bookkeeping capabilities and reduce the potential of accounting record intrusion. They also allow shareholders to participate in decision-making in a transparent manner. They also aid in trade clearing, when payments are transmitted once trade settlement amounts have been computed.

Smart Contracts in Warehouse Receipt Lending:

To improve and sustain the living conditions of marginal farmers by bringing negotiation and reducing various agriculture-related frauds, a blockchain-backed lending platform can play its role. It can help banks reduce the fraud risk in Warehouse receipt Finance and provide timely access to credit to Farmers and other stakeholders. This may assist farmers in obtaining the best price for their crops during harvesting. Lending can be done through a consortium that includes financiers, banks, and other stakeholders. On a single platform, the blockchain network with mobile app links banks, warehouses/collateral managers, and borrowers. Using blockchain’s tokenization and immutability capabilities, the network decreases lending risk for banks while smart contracts enable other participants boost efficiency.

We have innovators working on this use case and it is being implemented at different levels. To know more about it please write to us at open-innovator@quotients.com