AD is a neurodegenerative disease commonly observed in old age/elderly people, manifested by gradual memory loss and impaired cognitive functions. A very significant advantage of the MRI scanned images are that they produce higher spatial resolutions and also, the image details are prominent for the disease diagnosis purposes. The most evident character of AD pathology is neuron loss, followed by brain atrophy progressing from AD signature regions (e.g. hippocampus and amygdala) to the entire cortical region, which can be identified by the MRI scan.

Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

The goal of this statistical analysis is to help us understand the relationship between house features and how these variables are used to predict house price. Objective Predict the house price Using two different models in terms of minimizing the difference between predicted and actual rating. We are creating a Django based website for real estate and in that we have added the prediction module to predict the house prices .

The idea behind this ML project is to build a model that will classify how much loan the user can take. It is based on the user’s marital status, education, number of dependents, and employments. We can build a linear model for this project. We are creating a Django based website for a financial institution and in that we have added the prediction module to predict the loan amount.

There are many datasets available for the stock market prices. This machine learning project aims to predict the future price of the stock market based on the previous year’s data. We are creating a Django based website for a stock broker and in that we have added the prediction module to predict the loan amount.

Transform images into emoji. Yes, the objective of this deep learning project is to Emojify the images. Thus, we will build a python application that will transform an image into its cartoon using machine learning libraries.

Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.

Breast cancer the most common cancer among women worldwide accounting for 25 percent of all cancer cases and affected 2.1 million people in 2015 early diagnosis significantly increases the chances of survival. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can dramatically improves the accuracy of diagnosis Research indicates that most experienced physicians can diagnose cancer with 79 percent accuracy while 91 percent correct diagnosis is achieved using machine learning techniques.

Brain tumor is one of the leading causes of cancer death. Accurate segmentation and quantitative analysis of brain tumor are critical for diagnosis and treatment planning. Since manual segmentation is time-consuming, tedious and error-prone, a fully automatic method for brain tumor segmentation is needed. Recently, state-of-the-art approaches for brain tumor segmentation are built on fully convolutional neural networks (FCNs) using either 2D or 3D convolutions. However, 2D convolutions cannot make full use of the spatial information of volumetric medical image data, while 3D convolutions suffer from high expensive computational cost and memory demand. To address these problems, we propose a novel Separable 3D ResU-Net architecture using separable 3D convolutions and the BRATS dataset.

Now-a-days, people face various diseases due to the environmental condition and their living habits. So the prediction of disease at earlier stage becomes important task. But the accurate prediction on the basis of symptoms becomes too difficult for doctor. The correct prediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has large amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the huge amount of medical data. We proposed general disease prediction based on symptoms of the patient. For the disease prediction, we use K-Nearest Neighbour (KNN) and Convolutional neural network (CNN) machine learning algorithm for accurate prediction of disease. For disease prediction required disease symptoms dataset. In this general disease prediction the living habits of person and check up information consider for the accurate prediction. The accuracy of general disease prediction by using CNN is 84.5% which is more than KNN algorithm. And the time and the memory requirement is also more in KNN than CNN. After general disease prediction, this system able to gives the risk associated with general disease which is lower risk of general disease or higher.

Neural networks are being used to improve all aspects of our lives. They provide us with recommendations for items we want to purchase, generate text based on the style of an author and can even be used to change the art style of an image. In recent years, there have been a number of tutorials on how to generate text using neural networks but a lack of tutorials on how to create music. In this article we will go through how to create music using a recurrent neural network in Python using the Keras library.

Sequence-to-sequence methods have achieved promising results for textual abstractive meeting summarization. Different from documents like news and scientific papers, a meeting is naturally full of dialogue-specific structural information. However, previous works model a meeting in a sequential manner, while ignoring the rich structural information. In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse, which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary. In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus, which can be used to pre-train our model. Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.

Everyone loves movies irrespective of age, gender, race, color, or geographical location. We all in a way are connected to each other via this amazing medium. Yet what most interesting is the fact that how unique our choices and combinations are in terms of movie preferences. Some people like genre-specific movies be it a thriller, romance, or sci-fi, while others focus on lead actors and directors. When we take all that into account, it’s astoundingly difficult to generalize a movie and say that everyone would like it. But with all that said, it is still seen that similar movies are liked by a specific part of the society. So here’s where we as data scientists come into play and extract the juice out of all the behavioral patterns of not only the audience but also from the movies themselves. So without further ado let’s jump right into the basics of a recommendation system.

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust ant spam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.

Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmias) and heart defects you’re born with (congenital heart defects), among others. The term “heart disease” is often used interchangeably with the term “cardiovascular disease”. Cardiovascular disease generally refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina) or stroke. Other heart conditions, such as those that affect your heart’s muscle, valves or rhythm, also are considered forms of heart disease. Heart disease is one of the biggest causes of morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subjects in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decisions and predictions. In this paper,we will discuss Machine Learning approaches (and eventually comparing them) for classifying whether a person is suffering from heart disease or not, using one of the most used dataset —Cleveland Heart Disease dataset from the UCI Repository. It consists of multiple features, including input clinical data section, ROC curve display section, and prediction performance display section (execute time, accuracy, sensitivity, specificity, and predict result).

Spotify, with a net worth of $26 billion is reigning the music streaming platform today. It currently has millions of songs in its database and claims to have the right music score for everyone. Spotify’s Discover Weekly service has become a hit with the millennials. Needless to say, Spotify has invested a lot in research to improve the way users find and listen to music. Machine Learning is at the core of their research. From NLP to Collaborative filtering to Deep Learning, Spotify uses them all. Songs are analyzed based on their digital signatures for some factors, including tempo, acoustics, energy, danceability etc. to answer that impossible old first-date query: What kind of music are you into? In this paper, we discuss the music genre classification using audio of the particular song using independent recurrent neural networks like LSTM and GRU. Consequently, multi-layer IndRNN is used as the main part of our model to classify music genres on the GTZAN dataset. In order to keep the information loss as less as possible, scattering transform is used to preprocess the raw music data. The experimental results show that the model achieves a competitive result in music genre classification task compared with the state-of-the-art models.

Social networking sites are the biggest source for big data collection due to their increased velocity and variety while generation of data. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we discuss about the paradigm to extract sentiment from Twitter which is a famous microblogging service with increasing audience. In this paper, we discuss the sentiment analysis of the existing twitter dataset. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. We will use Natural Language Processing models for classifying the sentiment of Twitter messages using distant supervision which is discussed in. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in. We examine sentiment analysis on Twitter data.

Credit card companies shall be able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. We are creating a Django based website for a Credit card company and in that we have added the credit card fraud detection module.

Bio Metric attendance management system and leave management system using Python Django/Tkinter . This system Is used to make daily attendance for School, Institute, College, University and Office. This system facilitates to access Attendance for specific person. This system is Divided into two parts. First part is admin part and second part is about facilitators. Main admin has full Rights to manage this system like to create new Employee, Student slots with specific ID. We are sharing This Software complete source code with you guys for learning purpose. With the help of this system you guys can create Department, Employee Scale and Add Salary, E OBI that is Dynamic. at the end of month.

An automating the task of scoring subjective answer is considered. The goal is to assign score which are comparable to those of human score by coupling AI technologies . Scoring is based on AI based parameter and natural language processing. System checks answer and score as good as human being. We are creating a Django based website for an institution to evaluate the answer using “Automatic Answer Checker”.

College Enquiry Chat Bot project will answer to student questions that is related to college. First bot analyzes user’s queries and understand user’s message, based on bot knowledge bot provide answers to the queries of the students. Student can query related to admission, faculty details, etc. Students won’t have to go to the college to make the enquiry. If any new candidate enquirers for admission and the details about any department of the college this bot will help to get the answer of query of the candidate. This project is available for Bank, Insurance Company also.

We live in the age of Instagram, YouTube, and Twitter. Images and video (a sequence of images) dominate the way millennials and other weirdos consume information. Having models that understand what images show can be crucial for understanding your emotional state (yes, you might get a personalized Coke ad right after you post your breakup selfie on Instagram), location, interests and social group. Predominantly, models that understand image data used in practice are (Deep) Neural Networks. Here, we’ll implement a Neural Network image classifier from scratch in Python.

Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.

IMDB is a large online database of information about movies, TV shows, video games among other content. For this analysis,We have focused on ratings for TV Series, TV miniseries, and episodes. The project emphasizes - are certain TV shows overrated or underrated, which TV shows are among the most or least consistent, and which TV series canceled too early and which ones went on far too long.

Social networks can be represented in a form of a graph, where the users are represented by nodes, and the relationships between them form the edges. Besides the most popular social networks like Facebook and Twitter, other types of networks can also be analyzed. Social networking sites are the biggest source for big data collection due to their increased velocity and variety while generation of data. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we discuss about the paradigm to extract sentiment from facebook which is a famous microblogging service with increasing audience. In this paper, we discuss the sentiment analysis of the existing facebook dataset. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. We will use Natural Language Processing models for classifying the sentiment of facebook messages using distant supervision which is discussed in. The training data consists of facebook messages with emoticons, acronyms which are used as noisy labels discussed in. We examine sentiment analysis on facebook data.

Unlike more traditional methods of machine learning techniques, deep learning classifiers are trained through feature learning rather than task-specific algorithms. What this means is that the machine will learn patterns in the images that it is presented with rather than requiring the human operator to define the patterns that the machine should look for in the image. In short, it can automatically extract features and classify data into various classes.

The price management process has to deal with many variables and use cases because pricing typically has a complex structure. As a basic example, consider a retailer who buys a certain product from a supplier at a supplier price, adds a markup to obtain a list price, optionally applies one or more markdowns, and finally accounts for variable and fixed costs to calculate the profit margin.

This project aims to create a sequence-to-sequence model that can detect metaphors/literal in the English language , given a large dataset of metaphors.

This research offers a semi-supervised machine learning method to identify fake/negative product reviews. In this work, various reviewer behaviours are extracted using feature engineering techniques.

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region.

This project is to predict the temperature using different algorithms like linear regression, random forest regression, and Decision tree regression. The output value should be numerically based on multiple extra factors like maximum temperature, minimum temperature, cloud cover, humidity, and sun hours in a day, precipitation, pressure and wind speed.

Artificial intelligence chatbots are chatbots trained to have human-like responses and conversations using a process known as natural language processing (NLP). With NLP, the AI chatbot is able to interpret human language as it is written, which enables them to operate more or less on their own.

The Devanagari script is one of the broadly used scripts of India and is advanced from the Brahmi script. It includes forty-seven number one alphabets, 14 vowels, 33 consonants, and 10 digits. There isn't any capitalization of letters, like in the English language. The Devanagari script includes consonants and modifiers.

Anomalous user behaviour detection is the core component of many information security systems, such as intrusion detection, insider threat detection and authentication systems. Anomalous behaviour will raise an alarm to the system administrator and can be further combined with other information to determine whether it constitutes an unauthorised or malicious use of a resource.

Deepfake is a kind of artificial media produced with the help of machine learning. It is applied to replace or even maliciously alter anyone’s likeness in pictures, videos, and voice recordings.

AI blogging is all about using the power of artificial intelligence-driven writing tools to create a blog. Such tools leverage advanced algorithms, machine learning, and natural language processing to craft compelling content for your blog.

The proposed method contains two main steps. The first step is region of interest extraction; it is based on the partition of the image into separate blocks to extract only the part that contains the hippocampus of the brain. The second step is the classification of images using two deep based techniques namely convolutional neural network (CNN) and Transfer Learning. In one hand, CNN allows extracting the characteristics from brain images, then classifies them into normal brain or AD brain.

Uses machine learning algorithms to predict and diagnose various diseases based on medical data, improving early detection and treatment outcomes.

A machine learning project that utilizes the Random Forest algorithm to predict the prices of houses or old cars based on features like age, location, size, and condition.

Machine learning techniques are applied to detect breast cancer in medical images (like mammograms), identifying patterns and abnormalities to support early diagnosis.

Convolutional Neural Networks (CNNs) are used to classify and identify medicinal plants from images, assisting in the identification of plants with potential health benefits.

This project uses machine learning models to detect fraudulent credit card transactions by analyzing patterns and anomalies in transaction data.

Machine learning algorithms predict whether a person is eligible for a loan based on factors like income, employment, credit score, and debt.

Involves embedding hidden information (like messages or files) within audio or video files, making it undetectable to the human senses while remaining retrievable with the right algorithm.

Converts written text into spoken words using Natural Language Processing (NLP) and speech synthesis technologies to aid communication and accessibility.

A secure system that prevents unauthorized access to email accounts by utilizing advanced authentication methods such as two-factor authentication or biometric recognition.

Analyzes user activity data (like web browsing or mobile app usage) to predict user intentions, optimize user experiences, and detect fraudulent activities.

A data mining project that identifies hidden patterns, correlations, and trends within large datasets, often used in areas like market analysis and fraud detection.

Machine learning is used to detect phishing attempts by analyzing URLs, emails, or websites for characteristics commonly associated with fraudulent activities.

A cryptography project using elliptic curve methods and BLS short signatures to provide a more secure, efficient, and compact digital signature solution for online transactions.

Ensures data privacy in cloud environments by implementing encryption techniques and access control measures, protecting sensitive data from unauthorized access.

Focuses on protecting data stored in the cloud through encryption, tokenization, and secure access controls, ensuring compliance with privacy regulations.

A digital locker service that allows users to securely store and manage important documents (e.g., certificates, IDs) in the cloud, providing easy access and sharing.

Implements K-Means or Hierarchical clustering algorithms to group similar data points in unsupervised learning tasks, often used in customer segmentation or pattern recognition.

Predicts the popularity of TV shows based on factors like ratings, reviews, genre, and viewership patterns, using machine learning models to forecast success.

Uses data mining techniques to analyze student performance, faculty data, and academic patterns to enhance decision-making and improve educational outcomes.

Analyzes purchase data from supermarkets to identify trends and popular products, assisting in inventory management and targeted marketing strategies.

A privacy-preserving data publishing technique that slices data into smaller segments to protect sensitive information while still enabling data analysis.

Involves extracting valuable insights from large text datasets and websites, classifying content, and identifying patterns and trends for business or research purposes.

Improves the K-Means clustering algorithm to enhance its accuracy and performance, often focusing on issues like initialization and convergence.

Uses techniques like generalization, bucketization, and slicing to modify data in ways that preserve privacy while allowing useful analysis.

A data aggregation technique that combines data from multiple sources or databases in a horizontal manner, often used in distributed databases and cloud computing.

A web crawler designed to retrieve and organize web data based on its semantic content, making it more efficient and relevant for specific topics.

Ensures privacy in distributed systems by applying cryptographic methods to perturbed (noisy or altered) data, allowing safe data sharing and analysis.

Analyzes passenger behavior to infer the purpose of their travels (business, leisure, etc.), improving transport services and user experience.

Extends the Apriori algorithm for frequent pattern mining in a distributed environment, adding privacy-reserving techniques to ensure secure data processing.

Combines multiple clustering algorithms to improve the accuracy and robustness of network intrusion detection and other internet security applications.

Identifies rare, sequential patterns in streams of documents (e.g., social media, news feeds) to analyze user behavior and emerging topics.

Focuses on creating methods to prevent keylogging software from capturing sensitive user input, often using encryption, obfuscation, and real-time monitoring techniques.

An app designed to support farmers with tools for weather forecasts, crop management, and direct access to government schemes and agricultural advice.

An app that allows users to report crimes or suspicious activities with features like location sharing, media upload, and direct communication with authorities.

A platform for better communication between parents and teachers, allowing updates on student performance, school events, and instant messaging.

A voice-controlled app that allows users to book cabs by speaking commands rather than manually typing.

A face recognition-based attendance system for schools or organizations, automating attendance without physical presence.

An app to help users identify plants using the camera and provide information about the plant species.

A health app that analyzes symptoms and provides possible diagnoses using AI or pre-set medical databases.

Offers personalized health tips, fitness plans, and diet recommendations based on user input and health data.

An encrypted messaging app focused on privacy, allowing users to send secure, encrypted SMS.

An app that uses AI to identify and provide information about objects captured in a photo or real-time video.

A simple app for recording and tracking attendance for classrooms or events.

A trivia app where users can answer questions on various topics, keeping track of scores.

Scans and decodes barcodes and QR codes, useful for shopping or accessing digital content.

Allows students and teachers to receive real-time updates on assignments, exams, or school events.

Uses image processing to recognize vehicle numbers for parking, toll collection, or law enforcement purposes.

An app for managing parking spaces, where users can scan QR codes to find available parking spots.

An app that generates QR codes for train ticket reservations and boarding passes.

A chatbot that provides general knowledge questions and answers in a conversational format.

A cloud-based locker to store and access digital documents securely with encryption.

An app teaching or simulating game theory scenarios, helping users understand complex mathematical concepts.

A simple and classic game where players try to get three marks in a row, either horizontally, vertically, or diagonally. It could be made more interesting with AI opponents or multiplayer modes.

An app that allows users to book, cancel, or reschedule appointments with service providers like doctors, salons, or consultants.

An app where users can book tables at restaurants, check availability, and receive confirmation or reminders for their reservation.

An app for tracking a patient's health status, medical records, appointment history, and medication schedules.

A cloud-based storage system for securely storing documents, such as ID cards, educational certificates, and other personal records, with easy access.

A system that tracks the location of school buses in real-time and provides updates to parents on their child's bus journey.

A ride-hailing app that allows users to book cabs, track the vehicle, and make payments through the app.

A secure SMS service that ensures private communication using end-to-end encryption, protecting users from potential threats.

An app for browsing and purchasing books online, with features like search, book reviews, ratings, and recommendations.

An app to book and manage medical appointments with doctors, hospitals, or clinics, allowing users to receive reminders and reschedule if needed.

An app that connects users with local "dabbawalas" (meal delivery services), allowing them to order and track their lunch deliveries.

An e-commerce app for browsing and purchasing furniture online, with features like product filtering, AR visualization, and home delivery tracking.

An online grocery shopping app with features like product search, shopping lists, order history, and delivery scheduling.

A system to automate food orders and payments at a school or office canteen, reducing wait times and streamlining the process.

An app for holding online auctions where users can bid on products or services, with features like real-time bidding and notifications.

An app for securely conducting voting in elections or polls, with a focus on privacy, authentication, and preventing fraud.

A simple app for calculating Body Mass Index (BMI) based on user height and weight, providing health recommendations based on the result.

The basic goal is to create a compact mobile device with the connections mentioned below. Initially, the engine will be ON by the power supply of the micro-controller, which means that the engine is running in user mode and doesn’t need any protection. After the user has parked the vehicle, the theft mode can be instantiated. If an intruder tries to start the vehicle i.e. if any unwanted vibration greater than a threshold value is detected or the vehicle is dislocated to a radius greater than the desired value ,a buzzer starts to ring by sensing activity using a piezoelectric sensor and an sms is sent to the owner stating that a break-in has occurred. The user can then take instant action by either informing the nearby police station or using some control functions like immobilising the vehicle from remote distance

If you want to know the temperature/Humidity of your home on the real-time basis, You can see it in your smart mobile whenever you want and where ever you might be the information reaches you with a single click. Is it not interesting, All such can be done with devices which report you. In Our project we used the DHT11 sensor for sensing temperature and humidity, Arduino microcontroller is used to control the devices and ESP8266 as Wifi module which has the facility to upload data to IoT cloud.

Every living organism needs food to live, We cannot imagine life without Irrigation. If everything gets automated even irrigation system also need to be. We have developed a Smart Irrigation system using the concept of IoT. As we know farming has different stages, To help these farmers in understanding the climatic conditions, moisture content in the soil and to make their decisions easy we have developed Smart irrigation System using IoT. We used different sensors that sense temperature, check water level before irrigation, moisture and humidity of the agricultural area. Information of all sensor nodes is collected by ESP8266 module and transmits the data to microcontroller connected with wifi module Node MCU that uploads the report on the cloud where a farmer can monitor those parameters in their Smartphones or PC on daily basis and take necessary steps to make their agriculture farm grow smart.

As the scope for Automation is increasing, many industries and organizations are looking over a better option that can be only IoT. This project helps you in weather reporting. We used many sensors that can help in sensing all-weather monitoring parameters and RaspberryPi gathers all the information from them and uploads it to the cloud which is a wonderful application platform for IoT, where you can see the report that sensors send you.

In many industries, Temperature is a very important parameter and they will be using big servers to store the monitored data on daily basis. Building a cloud-based temperature monitoring system is the best way to store data on a cloud and can have access to monitor the temperature anywhere in the world.

Building a cloud based Fire monitoring system is very important to reduce the cost of maintaining servers, to avoid data losses and to make the access easy with multiple internet connected devices ( computer, tablet, mobile phone) at the same time anywhere in the world. Using Internet of Things (IOT). Here, we are going to design a fire detection system and data to be upload it to a cloud storage.

This project uses water level monitoring using ultrasonic sensor,Data was sent to clould ,you can turn on or turnoff the valve of particular tank online .

In this proposed system, we are having database of authorized person list by registering their faces by entering OTP, so that non authorized person can’t able to enter the home until they enter the correct OTP. Whenever some person pressing calling bell switch, the camera gets triggered and capture the image of the intruder and checks that the image to the database, if that face is not matching with the database, it sends an email containing that intruder image and OTP, when intruder type the OTP by the owners’ knowledge then it authenticate the user to enter.

It allows the sensor to detect various colors at different distance with only one calibration and does not require calibration with eight colors before its use in measurement system.

This website deals with students wish to choose different career option using their academic score . Student registers , enters their academic details , cet ,neet, jee scores, after they get a list of colleges they can take admission with their score. Project can be operated by Admin and Student. Admin can add college details, stream details, and cut off for different degree Engineering, MBBS,Diploma , Bsc etc.

This project is a website manages college activities starting from Contact Us, admission, exam , result , activity management etc. Project can be operated by Admin , Faculty and Parents . Admin add Stream , Faculties , fees etc. Faculty add Subjects, conduct exams, results, events etc. Parents can check event gallery, attendance , exam time table and result.

This project is a website manages college activities starting from Contact Us, admission, exam , result , activity management etc. Project can be operated by Admin , Faculty and Parents . Admin add Stream , Faculties , fees etc. Faculty add Subjects, conduct exams, results, events etc. Parents can check event gallery, attendance , exam time table and result.

This project deals with employee salary calculation , supports graphics. Project handles employee registration, department, salary details and finally payslip generation. Project can be operated by Admin , Employee . Admin can add employee and other salary related details, employee can view payslip.

This website deals with college library . Project handles member details , membership, books , issue, return and fine calculation.Project can be operated by Librarian . Librarian can add member, add books, issue books , records returned book details and also fine is calculated in case of delay. Various daily and monthly reports are also generated using crystal reports.

This website deals with video tutorials kept for different categories like beauty care , skin care, hair care etc. User can upload and download video of their choice. Admin can add , remove videos, user and upload and view videos.

This project is a e-commerce website manages beauty products for different categories line Hair Oil, Skin Care, Cosmetics products etc and sells these products. User registers, browse product, add product to cart, browse combo offer, make payment. User can cancel order before a stipulated delivery date .Project can be operated by Admin , User . Admin add new products , checks order details , delivery details, payment and invoices.

This website deals with Buyers and Sellers who wish to sale purchase used electronic products. Seller registers, add their product for auction for a specific period, the buyer bids a product and buyer with highest bids win the product , buyer make the payment and take the product . Project can be operated by Admin , Buyer and Seller .

This project deals with hospital , supports graphics . Project handles patient registration, appointment , doctor registration , check patient appointment list on daily basis etc. Project can be operated by Admin , Doctor .

This website manages story writing, story moderation , approval, rejection , finally publish the story in college magazine. Project can be operated by Admin , Moderator and User . Admin add Moderator , view a list of story accepted and rejected. Moderator approves or rejects a story by going thru the contents, videos, images. Story writing section supports graphics and multimedia.

This project is a e-commerce website manages ayurvedic product online selling . User regisrers, browse product, add product to cart, browse combo offer, make payment. User can cancel order before a stipulated delivery date .Project can be operated by Admin , User . Admin add new products , checks order details , delivery details, payment and invoices.

This project is a website manages school activities starting from Contact Us, admission, exam , result , activity management etc. Project can be operated by Admin , Teacher and Parents . Admin add class , section, teachers , fees etc. Teacher add exam, result details. Parents can check activity details , exam time table and result.

This project deals with hostel room booking facility for any college supports graphics. Project handles room booking,cancellation, food package selection , billing . Project can be operated by Admin and User. Admin can add room details, food package details and process monthly bill .User or student can book a room of their choice and select food package and pay the bill at the month end.

This project is for a bank , supports graphics.Modules are Add edit customer, account, login , Add, edit, search account details, manage deposit, withdrawl , and view transaction details customerwise . Project can be operated by Admin and Customer .

This project is for a library , supports graphics.Modules are Add edit library member, Add, edit, search book details, manage system users, login, issue book to members, receive book from members, view book status, view transaction report. Project can be operated by Librarian .

This project deals with hospital , supports graphics . Project handles patient registration, appointment , doctor registration , check patient appointment list on daily basis etc. Project can be operated by Admin , Doctor .

This project deals with employee salary calculation , supports graphics. Project handles employee registration, department, salary details and finally payslip generation. Project can be operated by Admin , Employee . Admin can add employee and other salary related details, employee can view payslip.

This project deals with railway ticket reservation supports graphics. User can enter source and destination city and date . A list of trains displayed. User can select a Train book seats for different Classes. Seats updation and daily booking done for every train .Project can be operated by Admin and User . Admin enters train , view bookings , cancellation etc. User registers and books ticket, make payment , cancel recervation and prints the ticket.

This project deals with hospital , supports graphics . Project handles patient registration, appointment , doctor registration , check patient appointment list on daily basis etc. Project can be operated by Admin , Doctor .

This project deals with hostel room booking facility for any college supports graphics. Project handles room booking,cancellation, food package selection , billing . Project can be operated by Admin and User. Admin can add room details, food package details and process monthly bill .User or student can book a room of their choice and select food package and pay the bill at the month end.

This project deals with video library supports graphics . Project handles member details , video of different Genre. User can take membership and issue return and the video , fine is also calculated. Project can be operated by Librarian . Librarian can add member, add video, issue video , records returned video details and also fine is calculated in case of delay.

This project manages school activities starting from Contact Us, admission, exam , result etc. Project can be operated by Admin , Teacher . Admin add class , section, teachers , fees etc. Teacher add exam, result details

This project deals with flight ticket reservation supports graphics. User can enter source and destination city and date . A list of flights displayed. User can select a flight book seats for different Classes. Seats updation and daily booking done for every flight .Project can be operated by Admin and User . Admin enters train , view bookings , cancellation etc. User registers and books ticket, make payment , cancel recervation .

This project deals with hotel room booking supports graphics. User can enter check in date and check out date , a list of rooms free for booking are displayed. User can book a room pay bill. Room updation done for every room .Project can be operated by Admin and User . Admin enters room details , update room status and view bookings . User registers and books room , make payment , cancel booking.

This project deals with automobile service center and booking of services. User can select services and book and pay bill. Project can be operated by Admin and User . Admin enters service type and charges , calculate bill, view bookings . User registers and books service , make payment , cancel booking.

This project is for shop. Customer enters basic details like phone no, and name then buy products , pay bills. Project can be operated by Admin and User . Admin enter/modify product details , update stock details and view sold products .

This project can be used by book store. Customer enters basic details like phone no, and name then buy books and accessories, pay bills. Project can be operated by Admin and User . Admin enters book details , update book details and view sold book details.