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Category: Artificial Intelligence

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Implementation of AI in Different Industries 

Implementation of AI in Different Industries 

This blog series explores the rapid transformation of various industries, including healthcare, finance, retail, and entertainment, by showcasing the impact and future potential of AI. 1. Healthcare Artificial intelligence (AI) is rapidly transforming industries across the globe, and healthcare is no exception. From pinpointing diseases earlier to streamlining administrative tasks, AI is making significant strides in improving patient care and outcomes. Let’s explore three exciting ways AI is revolutionizing healthcare: 1. Early Disease Detection with AI Image Analysis: AI systems now analyze medical images like MRI scans to detect early signs of diseases, such as cancer, with remarkable accuracy. This leads to earlier diagnoses and significantly improved patient outcomes. 2. Personalized Medicine: AI is enabling personalized medicine by analyzing patients’ unique genetic profiles to create customized treatment plans. This approach is particularly effective for diseases like diabetes, resulting in more effective medications with fewer side effects and an improved quality of life for patients. 3. AI Manages the Back Office Operation AI streamlines administrative tasks, such as scheduling appointments and managing patient records, freeing up doctors and nurses to focus more on patient care. This efficiency reduces the burden of mundane tasks and enhances the overall healthcare experience. 2. Finance AI is revolutionizing finance by enhancing money protection and banking experience, transforming three key areas: safeguarding money, streamlining banking, and transforming the financial sector. 1. AI-powered Fraud Detection: Advanced AI systems monitor financial transactions in real-time, detecting and preventing fraud by identifying suspicious patterns, safeguarding both banks and customers from financial losses. 2. AI Traders and Predictive Analytics: AI analyzes vast market data to identify trends and predict movements. This enables high-frequency algorithmic trading, optimizing investment strategies and maximizing returns for investors. 3. AI Chatbots: 24/7 Financial Assistance: Banks use AI-powered chatbots for instant customer support. These virtual assistants answer questions, guide transactions, and offer personalized financial advice, improving customer satisfaction and streamlining banking services around the clock. 3. Retail Artificial intelligence (AI) is revolutionizing retail by ensuring stock availability and providing personalized shopping experiences, transforming the way we shop and interact with products. 1. AI-powered Forecasting: AI predicts product demand by analyzing sales data and trends, automating stock replenishment to prevent stockouts and excess inventory, enhancing customer satisfaction, and optimizing the supply chain. 2. AI-powered Personalization: AI recommendation engines tailor the shopping experience to individual preferences by analyzing past purchases and behavior. This helps customers discover new items and boosts sales and engagement for retailers. 3. Supply Chain Optimization: AI streamlines deliveries by analyzing demand patterns and predicting future needs. It identifies efficient delivery routes, reducing delivery times and transportation costs, benefiting both retailers and customers. 4. Manufacturing AI is revolutionizing the manufacturing industry by enhancing efficiency, preventing equipment failures, and ensuring high-quality products, making the factory floor a modern, intelligent space. 1. Predictive Maintenance: AI analyzes sensor data from machines to detect potential failures, enabling proactive maintenance. This prevents costly downtime and ensures smooth production runs. 2. AI-powered Quality Control: AI vision systems inspect products with precision and speed, identifying defects to ensure consistent quality. This reduces waste and improves overall production efficiency. 3. Production Process Optimization: AI analyzes production data to optimize resource allocation and streamline operations. This enhances production efficiency, helping manufacturers maximize their resources and improve performance. 5. Transportation AI is revolutionizing transportation, enhancing safety, efficiency, and excitement through self-driving vehicles, intelligent traffic management systems, and reimagining transportation for a more exciting and efficient journey. 1. Self-Driving Revolution: AI enables autonomous vehicles to navigate and make safe driving decisions, reducing accidents and improving road safety. In logistics, AI-powered trucks optimize delivery routes and cut transportation costs. 2. AI-powered Traffic Flow Management: AI analyzes real-time traffic data to optimize traffic light patterns, reducing congestion and improving travel times. This enhances daily commutes and lowers fuel consumption and emissions, contributing to a greener environment. 3. Predictive Maintenance: AI predicts vehicle maintenance needs by analyzing sensor data, allowing proactive scheduling to prevent breakdowns and minimize downtime. This improves fleet reliability and saves time and money for operators. 6. Education AI is revolutionizing education by enhancing personalized, efficient, and engaging learning experiences for students of all ages, from personalized lesson plans to intelligent virtual tutors. 1. AI-powered Personalization: Adaptive learning platforms use AI to tailor lessons to individual needs, allowing students to progress at their own pace. This personalized approach improves learning outcomes and engagement. 2. AI Handles the Mundane: AI systems take over administrative tasks like grading and managing student records, freeing teachers to focus on inspiring and guiding students, enhancing the overall educational experience. 3. Interactive AI-powered Tools: AI creates immersive learning experiences, such as virtual reality explorations and real-time virtual tutors. These tools make learning more engaging and help students retain information more effectively. 7. Agriculture AI is revolutionizing agriculture by improving precision, productivity, and sustainability, optimizing irrigation, predicting crop yields, and fostering a brighter future for farmers. 1. Precision Farming: AI analyzes sensor and aerial data to recommend precise amounts of water, fertilizer, and pest control, reducing waste and increasing crop yields for sustainable farming. 2. Predicting Future Harvests: AI-powered analytics predict yields by analyzing weather patterns, historical data, and crop health. This helps farmers plan harvests, adjust planting schedules, and manage weather-related risks for maximum harvest potential. 3. Agribots: AI-driven autonomous tractors and drones are transforming agriculture. Drones precisely spray pesticides, and robots plant seeds efficiently, increasing productivity and reducing labor costs, allowing farmers to focus on other critical tasks. 8. Entertainment AI is revolutionizing the entertainment industry by creating personalized music playlists and intelligent in-game characters, making it more engaging, interactive, and fun. 1. AI-powered Playlists: AI music platforms analyze your listening habits to create personalized playlists matching your mood and preferences, ensuring the perfect soundtrack for any occasion without endless scrolling. 2. Enhanced Gaming: AI in gaming creates adaptive non-playable characters (NPCs) that learn from player behavior, providing challenging and engaging gameplay. This makes games more immersive with lifelike characters and dynamic strategies. 3. Streamlined Film Production: AI assists in filmmaking

Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence vs Machine Learning vs Deep Learning

Let’s resolve the biggest confusion between all comprehensive technical terms like artificial intelligence, machine learning and deep learning. Artificial intelligence is a broader space under which Machine Learning and Deep Learning are subsets of it.   Let’s understand briefly about each of them to get a better picture. The concept of artificial intelligence came into existence in 1956. But data at that time was not sufficient to calculate accurate results. Artificial Intelligence   Artificial Intelligence is a technique by which machines demonstrates intelligence or behavior like humans. In AI, machine can learn from experience, like new born kids do. So from new input data, machine can adjust new responses. We can consider artificial intelligence as a project of creating Huge Monument which can take centuries to build. So the one who started building it, could not even see it fully built. AI researchers started working on bricks and bases of the project by creating learning algorithms so that future researchers will use it to build smart intelligence system. Example of Artificial Intelligence: Apple Siri, Microsoft Cortana, Tesla Self Driving Cars and many more. Machine Learning In Artificial intelligence, it was difficult to train complex decision making operation models of the Human brain. “Machine Learning is an application Artificial intelligence which enables machine to learn from statistical data to improve with experience.” The designed algorithms in Machine Learning are developed in such a way, that it can learn and improve the results when new data is provided. Example of Machine Learning: Netflix, Google Maps Elaborate Examples: Netflix – Depending upon what type of movies and series you watch, Netflix will suggest you same type of movies and series to you in Recommended section. Google Maps – Google map analyses the traffic and suggests you the fastest routes to your destination. Deep Learning Deep learning is a part of a broader family of Machine Learning that is inspired by the functionality of our brain cells called artificial neural network. It takes data connection between all the artificial neurons and adjust it according to the data parent. With the increase in the size of data parent ,more neurons is  added . You can relate Deep Learning as rocket a rocket engine which uses huge amount of data a fuel to process the algorithms. Deep Learning concept is not new but recently it’s hype has increased and getting a lot of attention. How Deep Learning Works at simple scale:   In above example, machine will validate all the criteria to check if the rectangle is a square. When it is nothing but nested hierarchy of conditions and checks. Deep learning does the same thing but a larger scale Above blog gives the brief difference of difference between different types of AI. Developing an application requires in depth knowledge and understanding of AI. If you want any assistant with AI application development you can always contact us. Techaroha Team is specialized in Block Chain and AI Application. Techaroha is One of the Best Software company to build application with a mix of Block Chain and AI.

Biggest Problem of India Solved with Pothole Detection System

Biggest Problem of India Solved with Pothole Detection System

Why ?? Pothole are a biggest hurdle in the development of the country.They harm citizens in directly or indirectly in many ways.Pothole Detection System can solve the Problem faced by Every Citizens of our Country. 1. 10% percentage of Fuel is loss due to Potholes causing an unnecessary loss of 8.5 Billion that is 56 thousand cores 2. Death Due to Potholes in India – 3597  , which is much more compare to any other unnatural death cause in India 3. Millions of Man Hours are lost due to people stuck in traffic because of Pothole. 4. Thousand Crores of Good Wasted Due to Potholes   Pothole the Biggest Problem . They are hurting Everyone knows what the hell potholes are. It’s one the biggest problem faced by every individual in the country. Whatever the way of commute citizen has they have to suffer because.  of Potholes. Pothole problem is so common in India and its being there for such a long time that normal people have stop complaining about , people have adopted pothole as the way of life. Damage because of Pothole Society Potholes has caused 3597 last year , which is much more that any other cause of unnatural deaths They create traffic jab wasting lot of human hours Damage Roads leads to burn more fuel leading to more pollution Jerk due to Potholes leads to several health problems including back issues and pain. India Rupees India import 85 Billion Dollar of fuel i.e 6 Lac Core (6,00000,00,00,000). 10% of fuels is wasted due to potholes.  We can save lot of this money and increase the rupee value Damaged to imported vehicles caused due to potholes further increase the dollar bill hitting rupee badly Goods Lots of perishable goods like soft fruits are wasted in huge quantity due to potholes. Vehicles life is reduce and their efficiency decrease due to potholes Percentage of damage goods increase compared to good roads. Why potholes are not fixed If potholes are such big problem why they are not fixed. With huge budget allocated to Municipal corporation the road could be fixed. Major question is why even the potholes to such deadly extends occurs.What’s the quality of roads. Is the road being checked and audited. There are many such questions. Whats the answer then. Lets understand why potholes are there in first place Road are faulty.Material is not good. It should be inspected after building. It should check check regularly to find the first occurrence of potholes Even a small pot holes is detected it should be fixed up as early as possible. But due to vast network of road many of the potholes is not noticed by authority and with the course of time it becomes to big. Pothole fixing is not up-to the mark.When a pothole is fixed authority should monitor first defect in fixing , so that authorities can know how good was the road pothole fixing. They can easily identify the good and bad contractor. Saving lot of taxpayers money.      Conclusion for Pothole Problem Conclusion is pretty simple monitoring the main issue. If road potholes and new road are properly monitored and  fixed on time many bad things like death , back pain , fuel wastage can be avoided. Over a period of time bad contractors and good contractors can be modified , corruption can be fixed  and we all will have good roads like any other foreign countries. Monitoring Roads with Humans is not possible, since its not accurate and it may lead to huge amount of corruption which is again a big problem of India. Only way out is to have a good automated system which will monitor the potholes in road and will inform the authority and will keep a log of data for future audit.We called it Pothole Detection System Solution to Pothole Problem in India “Monitoring road with a Fully Automated Drone with Artificial Intelligence (AI) and Deep Learning Algorithms.” i.e Pothole Detection System Above system can solve one of the big problems of our country i.e Pothole. Eventually it will reduce fuel consumption , reduce road accidents , reduce traffic and save man hours. Detail Explanation Road and Pothole Detection System will require 3 Different System Drone Web Application for Interaction Artificial Intelligence Drone for Pothole Detection System A road will be decided for monitoring. The map of road will be feeded into the road. Road may be of 50 to 100 KM or More. Drone will navigate the road in regular interval of time. Navigation Timing of Drone Navigation by Drone can be timely base i.e they will monitor the road every 7 Week. Drone can be triggered on the completion of Event. Event like a Pothole is fixed , Pothole Contract is completed , Road Maintenance Contract is completed etc.   Drone will travel as per the feed map and will record videos. Videos will be feeded to the Artificial Intelligence and Deep Learning  Layer. This Deep Learning system will identify the potholes. As soon as pothole is detected, Drone will record the geo coordinates of the pothole location and date and time of detection. Details of pothole along with geo coordinates  will be sent to Back End , which can be used to authority.Authority can view all the potholes detected by AI System. Drone will also be fitted with laser measurement system. With the laser system the depth of severity of the potholes , its dimension can be known. It will help to define the severity and priority of the Pothole. It will help authority to act on orderly fashion and get rid of more dangerous pothole with highest priority. Machine Intelligence and Deep Learning Layer Machine  Intelligence and Deep Learning Layer will help  help to find the path hole with Image Processing and Feature extraction. Here we are using two branches of Artificial Intelligence. Deep Learning Deep Learning will help to extract the features from image and video and will help to identify the potholes. This Deep

Getting started with Machine Learning – Node.js integrated with Google Video-Intelligence

Getting started with Machine Learning – Node.js integrated with Google Video-Intelligence

Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the datasets given. In this article, we are going to cover a simple example of Machine Learning by integrating “Google Video-Intelligence” API with Node.js application. What is Google Video-Intelligence? Google under the project of Machine Learning has introduced Video-Intelligence. This makes video searchable and discoverable by extracting contents of a video with an easy to use REST API. furthermore, you can read more about google video intelligence on the google page. Prerequisites for Machine Learning Basic knowledge of Node.js applications Basic knowledge of Google Cloud Platform Step 1: Enable the Google Video-Intelligence API Sign into your google account with valid user credentials In the Google Cloud Platform Console, go to the Manage resources page and select or create a new project. Make sure that billing is enabled for your project. Also, you can find guidelines on the google docs link. Enable the Cloud Video Intelligence API. Step 2: Authenticating to a Cloud API Service To allow your application code to use a Cloud API, you will need to set up the proper credentials for your application to authenticate its identity to the service and to obtain authorization to perform tasks. The simplest authentication scheme is that of an API key. However, an API key does not allow you to authorize to the service, so it is only usable on public data or data you pass directly to the RPC API. Set up an API key After you enable a Cloud API, Go to “API & Services” through navigation menu. Click “Go to Credentials” to click on the Create Credentials button. Select “API key” from the options. You may wish to copy your key and keep it secure (you can also retrieve it from the API Manager→Credentials page). Set up a service account for machine learning Google Cloud Platform API authentication and authorization (commonly grouped together as “auth”) is typically done using a service account. in addition, A service account allows your code to send application credentials directly to the Cloud API. Go to “API & Services” through navigation menu. Click on “Create Credentials” and select “Service account key”. Select service account as “Compute engine default service account”, select key type as JSON and hit the Create button. Finally, GCP Console will generate a JSON key (as a .json text file), prompt you to download the file to your computer. The generated JSON key will be similar to the following sample JSON key: { “type”: “service_account”, “project_id”: “project-id”, “private_key_id”: “some_number”, “private_key”: “—–BEGIN PRIVATE KEY—–\n…. =\n—–END PRIVATE KEY—–\n”, “client_email”: “<api-name>api@project-id.iam.gserviceaccount.com”, “client_id”: “…”, “auth_uri”: “https://accounts.google.com/o/oauth2/auth”, “token_uri”: “https://accounts.google.com/o/oauth2/token”, “auth_provider_x509_cert_url”: “https://www.googleapis.com/oauth2/v1/certs”, “client_x509_cert_url”: “https://www.googleapis.com/…<api-name>api%40project-id.iam.gserviceaccount.com” } Install and initialize the Cloud SDK Download appropriate google cloud SDK to your OS platform. The installer starts a terminal window and runs the gcloud init command in windows (for other operating systems, manually run the same command). Go through the online guide for detailed information. Provide authentication credentials to your application code by running the following command. Replace [PATH] with the location of the JSON file that contains your credentials. gcloud auth activate-service-account –key-file=[PATH] Obtain an authorization token using the command: gcloud auth print-access-token Copy the access token at somewhere safe. Step 3: Integration with Node.js Application Considering you have already a basic Node.js application ready, we will explain how to use google REST APIs. In order to use video-intelligence, we need to install npm package containing client library. Open the command prompt in the project directory and install google cloud library using the following command npm install –save @google-cloud/video-intelligence Once the package is installed, we will write a code providing local storage video URL to the function. This function will convert the video into the base64 format and pass to the google video intelligence API. Code for Implementation // Imports the Google Cloud Video Intelligence library + Node’s fs library const video = require(‘@google-cloud/video-intelligence’).v1; const fs = require(‘fs’); // Creates a client const client = new video.VideoIntelligenceServiceClient(); /** * TODO(developer): Uncomment the following line before running the sample. */ // const path = ‘Local file to analyze, e.g. ./my-file.mp4’; const path = ‘avengers_trailer.mp4’; // Reads a local video file and converts it to base64 const file = fs.readFileSync(path); const inputContent = file.toString(‘base64’); // Constructs request const request = { inputContent: inputContent, features: [‘LABEL_DETECTION’], }; // Detects labels in a video client .annotateVideo(request) .then(results => { const operation = results[0]; console.log(‘Waiting for operation to complete…’); return operation.promise(); }) .then(results => { // Gets annotations for video const annotations = results[0].annotationResults[0]; const labels = annotations.segmentLabelAnnotations; labels.forEach(label => { console.log(`Label ${label.entity.description} occurs at:`); label.segments.forEach(segment => { let time = segment.segment; if (time.startTimeOffset.seconds === undefined) { time.startTimeOffset.seconds = 0; } if (time.startTimeOffset.nanos === undefined) { time.startTimeOffset.nanos = 0; } if (time.endTimeOffset.seconds === undefined) { time.endTimeOffset.seconds = 0; } if (time.endTimeOffset.nanos === undefined) { time.endTimeOffset.nanos = 0; } console.log( `\tStart: ${time.startTimeOffset.seconds}` + `.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s` ); console.log( `\tEnd: ${time.endTimeOffset.seconds}.` + `${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s` ); console.log(`\tConfidence: ${segment.confidence}`); }); }); callback(labels); }) .catch(err => { console.error(‘ERROR:’, err); }); After giving the request some time (about a minute, typically), the same request returns annotation results in JSON format. Congratulations! You’ve sent your first request to Cloud Video Intelligence API. Note:- Google Video-Intelligence API is not a free service. You can get free credits for demo purpose. You want any assitance or want to develop you application in Machine Learning you can always contact us here. Resource- Google Video-Intelligence Docs

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