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Benefits of Hybrid Mobile App Development

In the beginning, when mobile app development was totally a new idea, it was ideal to build native mobile applications. However, now with the maturity and growth of mobile-based users, more application usage, and fragmentation of devices, they have come to recognize the advantages of multi-platform app development.

Hybrid mobile applications, inevitably, fall in the middle of the native and web application gamut. They are clear with the user experience elements and features of both web and native domains offering an assortment of uplifting benefits.

The Need for Hybrid Application Development

A hybrid application is not restricted to a picky operating system. It is written by using the standard web technologies including HTML5, JavaScript and CSS, along with 3rd party products to effectively “wrap” the code. They are further used as a native application for diverse mobile operating systems. Here it’s a need that a single application is developed just once and then deployed to numerous device categories.

For instance, a web-based application coded in HTML5, JavaScript and CSS can be united with a product such as Cordova and PhoneGap. These modernized products “wrap” the needed web application and output the application for manifold mobile operating solutions as well as required systems. The application is then made accessible at different stores including iTunes and Google Play.

Hybrid Mobile Application Development Features

  • They support portability – one code platform and can be utilized on multiple platforms
  • You can manage several hardware and software features by using diverse plugins
  • Cost-effective mobile application development environment for all types of stakeholders
  • Swift and quick way to build mobile applications with multiple features and functionalities

Advantages of Hybrid Mobile App Development

1. A Decrease in Development Costs

Developing a hybrid mobile app is comparatively cost-effective and gets the job done quicker relatively than any other native or web mobile application. In the intensely competitive digital world where ‘time to market’ has turned significant than continually, cost efficiency plays an essential role in assisting enterprises to create and get their product to the market in not much time.

With the assistance of a set of libraries and multiple development frameworks which include the latest ones such as Xamarin and PhoneGap, hybrid application developers can speed up the development procedure and submit the application to a range of app stores to in fact save efforts, time and overall costs

2. Enhanced UI/UX

A reliable user experience across multiple mobile platforms is one of the main rationales behind hybrid app’s recognition. Users anticipate the app to be right away responsive on diverse devices and set free a glitch-free experience.

Hybrid applications are based on the inspiration of “information is just a knock away.” And while it exhibits data speedier and fiddle with to changed device screen configurations instantly, it also solves the problems of the random data streaming abilities. It is also lightweight and so the hybrid app UI can be easily be loaded with the high-definition graphics and useful content.

3. Effortlessness Integrations

Similar to native apps, hybrid applications drive the device’s inner programming solution by an overlay which assists to deliver better synchronization with other well-suited apps. This decreases the integration problems for developers.

Again going around, the hybrid application works smoothly with the device’s native apps covering camera, messaging and GPS to make sure a better user experience.

4. Useful Offline Data & Information Support

Hybrid apps store the device’s API to save offline information and data that further helps to load the app swiftly. It moderately stores information that the users can obtain during poor or when there is no connectivity.

Since the majority of users want to trim down their mobile data usage and have nonstop access to application data, a hybrid app is competent of offering just that – offline app convenience without a performance drop down. It is one of the chief reasons why hybrid mobile apps are the most excellent when evaluated with native mobile applications.

5. Simple to Maintain and Sustain

Unlike a web application, a hybrid app is intended to make use of all the accessible features on hand in the mobile device. Despite the fact that native apps also use all the device functionalities and features, maintaining it is somewhat a challenge for users and developers. Developers need to roll out newer updates and fresh versions. On the other hand, users are needed to update the application each time a novel version is launched in the market.

A hybrid application bypasses versioning and makes app upholding as trouble-free as updating a website page, that also on a real-time basis. This level of suppleness further enables the scalability requirements of an enterprise.

Key Takeaways

A mobile app is a vital tool for enterprises to make a way into the market swiftly and remain competitive. And a hybrid application solution makes this job straightforward as well as speedier.

Giant organizations like Twitter, Uber, and Instagram have already driven their performance with the benefits of hybrid mobile app development. If you too are looking to make the most of this technology via a hybrid app, we can quickly connect and discuss your requirements today itself.

Written By : Technostacks

Deep Learning With Python

Way back in the year 1991 when the great Guido van Rossum had released Python as his side assignment, he had not expected that python would become the world’s fastest developing computer language of the near future. If the trends are to be believed, Python turns out to be a go-to language for the fast prototyping.

If an individual dwells deep at the philosophy with which the Python language is created, one can say that the language had been built for the purpose of its readability and its less complex nature. One can easily understand the language as well as make someone else also understand the same very fast.

The language Python is successful at winning the hearts of its users. As per the Hackerrank 2018 developer survey, it is believed that the JavaScript might be the top most in-demand Programming language by the employers, but the Python language has won the heart of the developers across all the age groups, as per the Love-Hate index.

Why deep learning in python?

It is essential to understand that why would someone wish to use only the Python language in designing any kind of deep learning project. Deep learning in layman terms, is the usage of the data in order to help a machine make intelligent decisions.

For instance — one can build a spam detection algorithm in which the rules may be learned from a data or an anomaly of detection of the rare events by observing at the previous data or by arranging the email based on the tags that one had assigned by viewing the email history and so on. The main task of deep learning is to simply recognize the patterns in a given data set.

One of the critical tasks of a deep learning engineer in his/her career life is to extract, refine, define, clear, arrange and understand the data that is given, in order to develop a set of intelligent algorithms. Thus for a deep learning engineer or a Computer Vision Engineer or a budding Data Scientist or a deep learning or an Algorithm Engineer or a Deep learning engineer one would definitely recommend Python, as it’s easy to understand.

Many times the concepts of topics such as Linear Algebra, Calculus are so complex, that they take a significant amount of effort. A simple implementation in the Python language helps the engineer to validate an idea. There are simple python deep learning tutorials available which offer the best possible assistance to language usage.

Data is the primary factor

Thus it entirely depends on the kind of the task where one wants to use deep learning. Let us take a view at a few instances and examples. For a computer vision projects, the input data is the image or the video. For a statistical review, it may be a series of points across time or a collection of language documents that are spread across the various domains or the audio files that are given or simply some numbers.

Try to imagine that everything which exists around is in the form of data. And the data is raw, inadequate, incomplete, unstructured, and large. Python can be a guide for deep learning to tackle all of the problems.

Python has a collection as well as code stack of the various open source repositories that is developed by the people (and still in process) for the purpose of continuously improving upon the existing methods.

That are very helpful for deep learning for beginner’s category of people. The following are some of the guide for deep learning in python:

  • In order to work with images — opencv, scikit and numpy
  • In order to work with text — nltk, numpy, scikit
  • In order to work with audio — librosa
  • In order to resolve the deep learning problem — scikit, pandas
  • In order to view the data clearly —  seaborn, scikit, matplotlib
  • In order to utilize the deep learning — pytorch, tensorflow
  • In order to perform scientific computing — scipy
  • In order to integrate any kind of web applications — Django

Deep learning in Python: the implementation matters

The total implementation of the clustering algorithm will open up insights towards the problem then simply reading the algorithm. In python, when a user implements the things, it is going to perform much faster in order to prototype code and then test it.

Key Takeaways

Thus it can be seen that if the focus is on the overall task that is needed to train, validate as well as test the models — so far as they satisfy the aim of a problem, any tool/language/framework may be used. Be it for the purpose of extracting the raw data from an API, or analyzing it, or performing an in-depth visualization and creating a classifier for a given task. But the primary reason for using deep learning in Python would mainly be its readability, versatility, and ease of understanding. You can cater your requirement to deep learning python experts to build an awesome application.

Written By : Technostacks

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When it comes to technological innovation and mobile development for your company, the sky’s the limit! Your vision is our reality, as we work with all of our clients to turn their ideas into real applications that serve to take their businesses to the next level. Our team specializes in mobile app, web, and AR/VR development with the goal to give companies the tools they need to survive in an increasingly technological world.

There is a common misconception that investing in your company online is a priority reserved only for larger companies and is an unnecessary step for smaller businesses. The problem here is that as companies are slower to change their practices, their customers are not. No longer are mobile apps the luxury of major brands, but a necessity for companies of all sizes to effectively reach customers and outpace competitors.

In our pursuit of delivering the best mobile app development solutions and building positive relationships with our clients, our team’s efforts have been recognized by Clutch, a D.C. based firm that conducts reviews on B2B service providers. After reviewing our company and learning about how we operate with our clients, we are noted on the Clutch platform amongst the best app developers and internet of things companies in 2019! This is a major accomplishment for our team and it’s extremely rewarding to know our hard work is not going unnoticed.

Check out the first review we have on our Clutch profile:
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On top of being included in Clutch’s research, Technostacks Infotech is also listed on their sister websites, The Manifest and Visual Objects, two new platforms that go deeper into the buyer’s journey, providing key industry reports, how-to guides, and curated directories of the best B2B service providers worldwide. On The Manifest, we’re listed amongst the best app development companies in Ahmedabad in 2019. On Visual Objects, buyers can get a firsthand look at the visual representations of our app development work in recent years.

We started our company to help companies make the most out of their businesses and utilize the online marketplace. We have an expert team of app developers who can able to build a successful mobile application for your organization. You can contact us for more information.

Through our presence on Clutch, it’s been extremely affirming to see how much our work has enabled our clients to grow their businesses. We look forward to all the new projects and opportunities to come in the new year!

Written By : Technostacks

Why You Should Use React JS

React JS was developed by Jordan Walke, who was an employee at Facebook. It is a Java framework. It is an open source JavaScript library which is used for building user interfaces for single page applications. It mains view layer for different mobile apps as well and also creates reusable UI components. It was applied on Facebook in 2011 and on Instagram in 2012.

We can create specific changes in data of web application without reloading the web pages. The purpose or say features of this is to be simple, fast efficient in creating a user interface for applications.

We can use it with another framework of JavaScript such as angular for this purpose. It has an active community and substantial foundation behind it and is a front-end library developed by Facebook.

To work with it efficiently, you must have vast knowledge about HTML5, CSS, and JavaScript. React JS doesn’t use HTML, but JSX is very similar to it. So being familiar it may help you to learn more. ES6 is a recent version of JavaScript which is used in this which makes it advance and more efficient.

Key Reasons to choose React JS

We have lots of framework platform so it’s a genuine question as to why we should use React JS. But it has some typical features which would make life easy for you. Let us look at some key reasons to choose React JS:

  • Simple and easy to learn: It is straightforward and sophisticated as compared to any other javascript frameworks and is neither difficult to understand and use. You can use plain JavaScript to create a web application and then handle it using this. You can mix HTML with it via some of its syntaxes. JSX is also much easier to use with it.
  • Code reusability and data binding: It supports code reusability and can create an Android web application. It uses one side data binding and also flux which is an application architecture which controls the flow of the data from one point. It is a useful feature regarding web application development and can help us a lot. Data binding and code reusability are essential factors.
  • Performance and testing: We can use to browse, ecmascript6 modules which define dependencies and can use it with reacts-di, babel, etc. They are easy to test can be treated as a function of the present state and can be checked from the output, triggered actions, events, etc. It is imperative to test before using and React JS makes it too easy to do it.

As discussed above the purpose of using React JS is to create user interfaces for web application with much ease and sophistication. It is the best framework when compared with others. It allows the user to perform the task with JSX rather than pure JavaScript, but you can use it too in case. It has native libraries developed by Facebook, and it gives reach architecture to android, UPD and IOS.

The benefits of React JS are as follows:

  • JSX is used which makes it more updated and quite simple to use. It uses HTML tags and syntax to render subcomponents .html tags are converted into react framework, and then the work goes on. Also can be done using simple JavaScript if JSX isn’t available.
  • Single Way Data Flow: It allows unique way data flow in which sets of values are passed as components rendered as properties in HTML tags. It cannot directly access or modify components but passes a call back which does this task. The property is then known as “Properties flow down, and actions flow up.
  • Virtual Document Objects Model: React JS creates components of memory data structures, which computes the changes and then updates the browser. So a unique feature is enabled which allows the user to code and it renders the components, elements, and data which can ultimately be processed and used.
  • Render method takes input and returns what to display. JSX is a XML like syntax. Components can be used to render () via these properties.
  • A state-full component: A component can maintain internal state data in addition to taking input data. When a components state changes, it is re-invoked by calling render (). Although event handlers seem to be rendered inline but will be collected and implemented using event delegation.
  • Comparison between Angular and React-JS: Subscription of HTML while React JS is a complete pure JavaScript based library. It is more advanced, simple, dependable and intensive programming than angular. Hence it is much better than angular when compared concerning the framework.
  • Can be applied using Babel: It is a compiler that converts markup language into JavaScript. You can use the newest features of JavaScript with this and also is available for different conversions. For example, our React JS uses this to convert JSX into JavaScript. JSX is an XML syntax extension to JavaScript which comes with full features of ECMAScript.
  • The JSX expressions can be used by rapping them in curly braces. They are immutable hence cannot be changed, you can just use render () to replace them in case of every time if you want modification anyhow.
  • React components are JavaScript functions. React uses ES6 classes to create components and can be created using the render method.

Key Takeaways

React JS is flexible and provides hooks that allow you to interface with other libraries and framework. It uses markdown libraries to do so. The declarative aspects make it more comfortable to debug as well. Overall react is the best framework for creating the user interface in a web application. When a website is complex to code and can’t define the understanding of a user, then one can go for React JS.

React JS is a better framework platform indeed to create a user interface for iOS, Android type web application. It is user-friendly, convenient, and efficient and why not it should be preferred over any other framework. It is applied in Facebook and Instagram. So if you are thinking of creating or modifying data on the web page then you must learn and use React JS.

If you have any question or planning to develop a react web application then you can hire us. We have experienced team of React JS programmers who are able to full fill your requirements.

Written By : Technostacks

Deep Learning In Healthcare

Deep learning is a part of the machine learning family. It is based on data learning representations and methodologies. In deep learning, there are three major classifications. They are supervised deep learning, unsupervised deep learning and semi-supervised deep learning. Deep learning is mainly used in recognizing speech, natural language processing, computer visions, social media networks, medical analysis, the design of the drug, bioinformatics, machine translations, game programs on board, and inspection of materials.

Data learning gets its inspiration from the processing of information and communication in medical nervous systems. The properties of data learning differ in structures and functionalities that make them incomplete. For example, the human brain. Each human brain is unique and differs in structure and functioning.

Deep learning in health care

To find out how deep learning can be used in healthcare, we must first look into the health care treatments offered by deep learning. So, Deep learning in health care is used to assist professionals in the field of medical sciences, lab technicians and researchers that belong to the health care industry.

Deep learning in health care helps to provide the doctors, the analysis of disease and guide them in treating a particular disease in a better way. So, the medical decisions made by the doctors can be made more wisely and are improving in standards.

Deep learning in various spheres of health care

  • Genomics – Deep learning technique is used to understand a genome and helps the patients undergoing treatment to get an idea about the disease that might affect them in the future. Genomics is a field which is steadily growing, and deep learning runs to have an excellent future with the help of insurance industries. Deep learning techniques are used to make medical field researchers and doctors fast and more accurate.
  • Cell scope – A Cell scope uses deep learning technology and it also helps the parents to keep monitoring the health conditions or the health status of their children. This technology can be seen on any devices and decreases the visits of parents to hospitals or health care centers to see their children or consult the doctor.
  • Insurance fraud – Deep learning is used in the analysis of medical insurance claim fraud. Deep learning uses a technique known as predictive analysis which can predict the fraud claims that are likely to happen in future. Deep learning helps the insurance industry to send offers and discounts to the target patients.
  • Medical imaging – There are a few techniques involved in the medical industry such as CT scan, ECG, MRI etc. that are used to diagnose harmful diseases. The harmful diseases include brain tumor, heart attacks, cancer, and many others. Hence, deep learning can be used to consult doctors who analyze the patient’s disease and provide them with good treatment.
  • Discovery of drug – Data learning helps in medicine discovery and is also used in developing them. A patient’s medical history is analyzed, and treatment is given according to the analysis. By the deep learning in health care, we can gain insight from the patient’s tests and reports and disease-related information regarding the symptoms.
  • Alzheimer’s disease – It is one of the most crucial challenges that medical industry clients are facing currently. Deep learning is used here to detect Alzheimer’s disease at its initial stage itself, making it more convenient for doctors to treat.

Deep learning solutions in health care and other industries

In deep learning, there are various hidden patterns and chances in helping doctors to treat their patients well. Machine learning, artificial intelligence has gained a lot of attention over the past few years. Now even deep learning has created its own path in this field and is steading growing in the market. Industries like health care, travel, and tourism, finance, retail and textiles, manufacture, health etc are relying on deep learning directly or indirectly. The first priority of any individual in today’s modern world is his or her health.

So, the health industry is one such platform that implements some technologies like deep learning for fulfilling their needs. Medical experts try to find various ways of implementing new technologies. These technologies must provide an impactful result for a better future. Deep learning puts together a bulk of data that included records of the patients, medical reports, personal data, insurance reports etc. for providing them with better treatment for a good outcome.

Current and future directions in health care and medical domains

Insights into deep learning in health care current and future applications are evident and can be clearly observed. Some of them are:

    • On gaining knowledge from complex, heterogeneous and high-dimensional bio-medical information, remains a challenge in transforming the health care industry. Different types of data are emerging in the modern world of medical sciences. Issues faced in imaging, sensor text, sensor data, electronic records etc. are solved by using deep learning algorithms. Deep learning makes the un-structured and complex examples into successful representations. The latest technology of deep learning provides new and efficient paradigms in obtaining the end to end learning models for complex data types.
    • Usage of electronic health records (EHR) promises to advance clinical research and better inform the decision-making skills clinically. Modern electronic health records can prevent the practice of predictive modeling by summarizing and representation. Patients who achieved results based on electronic health record data and an alternative feature learning strategy are being successful. From various research’s we can straight away say that deep learning framework arguments decision systems in the clinical environment.
    • Food and drug administration have finally approved the plasminogen activator streptokinase and urokinase and therapy can no longer be an approach for treating the thrombosis disease. Deep learning is used in treating acute peripheral arterial thrombosis and embolism along with acute coronary thrombosis.
    • Deep learning is a family of many computational methods that allow any algorithm that programs itself by using a learning method from a large set of examples that demonstrate the desired behavior. As an application of this method, there is a further assessment and validation to medical imaging.
    • Deep learning trained algorithms have some outcomes and measures. The specificity and the sensitive nature of the algorithm in detecting reference diabetic retinopathy (RDRs) can be defined as moderate and also as the worst ones. So deep learning trained algorithm was evaluated at two operating points which are selected from a development set out of which one handles high specificity and another one handles high sensitivity thereby providing better results.
    • Electronic health recorder or EHR with predictive modeling Dara is anticipated to solve personalized health quality in medicine. So, for constructing predictive statistical method, we require extraction of curated predictor variables from EHE data that is normalized and a process that discards the majority of information in each patient’s record.Deep learning after using this representation can be capable of predicting multiple medical events from various medical centers without any site-specific data harmonization. Data learning approaches can be used to create an accurate and scalable prediction for a variety of sceneries regarding clinical resources. In deep learning, neural networks can be used for identifying relevant information from the patient’s chart and records.
  • Data learning algorithms are convolutional networks that have become a methodology by choice. They are being used to analyze medical images. Deep learning can further be used in medical classification, segmentation, registration, and various other tasks.Deep learning is used in areas of medicine like retinal, digital pathology, pulmonary, neural etc. Deep learning is a steadily developing trend in the field of data analysis and has also been named one of the 10 breakthrough technologies in the year 2013. Deep learning is an advancement of artificial neural networks which consist of more layers at higher levels of abstraction.

    There is an improvement in the predictions from the data developed using deep learning algorithms. Deep learning is emerging as a very important machine learning tool in imaging, convolutional neural networks, computer domains vision etc.

  • Health informatics is emerging as a domain of interest among researchers all over the world. Therese researchers owe the majority of the implications on society. Right from the prediction of a disease to providing personalized services to patients, applications in deep learning range. Biomedical data in the health care industry has gained knowledge about many applications based on techniques followed by deep learning.
  • The health care field of modern era comprises various strategies that are of national importance owing to their spectrums of reach to individuals or society. We have earlier witnessed the advancements in machine learning and artificial intelligence technologies applied in various domains. Among all the domains, health care is in the primary focus of deep learning and machine learning researchers and experts of the industries owing to the veracity of data availability and high volumes.
  • The increase of health care sectors is being characterized by large data sets from clinical management systems. This provides a choice for any application of deep learning approaches on health care data sets, which may be sparse.

Benefits of deep learning

There are various benefits of deep learning in the health care industry. Some of them are:

  • Deep learning learns the important relationships in your data and records the information about past clients which can be used a future reference for the patients with similar symptoms or diseases.
  • Deep learning allows us to create a model-based on whatever source of data available when you require a risk score upon administration other than discharge.
  • Deep learning provides accurate and timely risk scores which enable the confidence and approximate allocation of resources.
  • Deep learning approaches lead to lower costs and provide improved outcomes.
  • When the deep learning algorithms interact with the training data, they become more precise and accurate allowing individuals to gain unprecedented insights into care processes, variability, and diagnostics.
  • Graphics processing units or GPU’s are getting more efficient to energy and are becoming faster.
  • Innovation is exploding as we now use deep learning algorithms at a fraction of the past costs and so algorithms are getting sophisticated.
  • Electronic health records or EHR and other digitization efforts are giving health care data the access to use trained algorithms than ever before.
  • Diagnostics are being more accurate and faster through deep learning which identifies patterns by connecting the tools.
  • Deep learning can determine whether the skin lesions are cancerous just like any other board certificated dermatologist.

Conclusion

Based on all the analysis done to deep learning by the different researchers, it is clear that deep learning can be an element for translating biomedical data into improved human health care. On the other hand, the latest advancements in deep learning technologies provide new useful and effective paradigms to prove the end to end learning models for uncertain and complex data structures.

Written By : Technostacks

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