Neomoment business for artificial intelligencePitch Deck AI.pdf

Neo moment is the best pitch deck that I have on my phone and it is why I am sharing this just in order to get the file I want but the business plan is actually management that think that they are employers meanwhile itw just a pitchseck

2. ABOUT US
GET TO KNOW US BETTER
Neomoment,aprominentAIandMLsolutionsproviderinIndia
andtheUAE,excelsindatascience,IoT,AI,andMLprojects.
Withofficesinbothcountries,weprioritizecollaborative
developmentandhaveearnedthetrustofprestigiousclients
likeGBMandAl-Jazeerathroughcreativityandcommitmentto
qualitywork.

3. Unattended Object
Detection
Developed to enhance security
in public and private spaces by
detecting unattended objects,
which could be potential security
threats. Algorithmsprocessed
video feeds to identify and label
objects within a scene and
monitored their status over time.
Objects remaining static and
unattended beyond a certain
timeframe triggered alerts.
Security in Monitored
Areas
Manifested a multifaceted
surveillance system, effectively
amalgamating speed, disease,
and unattended object detection
into a unified solution, which
significantly uplifts safety, health,
and security standards within
monitored environments
1) INTEGRATED SURVEILLANCE SYSTEM WITH SPEED, UNATTENDED
OBJECT DETECTION
This project centered on developing a holistic surveillance system capable of detecting vehicle speeds, and pinpointing
unattended objects through meticulously crafted algorithms
Speed Detection
Developed to manage and
regulatetraffic flow, ensuring
adherence tospeed limitsand
safeguarding pedestriansand
motoristsalike. The algorithm
processed video frames,isolated
moving objects (vehicles), and
calculated their speedby
evaluating the distance covered
in a predefined time frame

4. Challenges Solutions
Utilized OpenCV for processing images,
Optical Character Recognition (OCR) for text
extraction, and to interpret graphical data used
CNN model ( Convolutional Neural Network )
Leveraged YOLO (You Only Look Once) for real-
time object detection and anomaly detection
algorithms to discern unattended items
Ensuring the algorithms function effectively in real-time
Techstack
A system that respects individual privacy while conducting
detailed surveillance.
Ensuring the algorithms function effectively in real-time
Maintaining high detection and low false positive/negative
rates
Incorporated facial and identity anonymization algorithms to
obscure individual identities during analysis
Employed edge computing to process data on-site, reducing
latency and ensuring timely responses.
Continually refined models with additional training data and
utilized ensemble learning to enhance predictive accuracy

5. 2) INNOVATIVE ENGINEERING DRAWING INTERPRETATION AND
VALIDATION SYSTEM USING COMPUTER VISION AND NLP/NLTK
Reducing manual workload
Ensured that the interpreted data adhered
to regulatory, industry, and design
standards by utilizing NLP to validate
textual annotations and specifications.
Developed a domain-specific language
model utilizing NLTKto comprehend and
analyze textual data extracted from
drawings. The system validatesthe data
by comparing it against predefined
standards and specificationsencoded
within the validation algorithms
High Accuracy
This project unveiled a system
that streamlines the
interpretation and validation of
engineering drawings,
serving as a potent tool to
enhance efficiencyand
accuracywithin engineering
workflows
The project focus on to create a system capable of reading, understanding, and validating engineering drawings by
marrying Computer Vision and Natural Language Processing (NLP) technologies
Reading/validation process
Developedalgorithmsto interpret
engineering drawings, including
understanding graphical
representations, symbology, and
textual annotations. Deployed
computer vision techniquesto isolate
and identify various symbols,
annotations, and geometries in the
drawings. OCRextracted textual data,
while CNNdeciphered graphical symbols
and patterns

6. Challenges Solutions
Utilized OpenCV for processing images,
Optical Character Recognition (OCR) for text
extraction, and to interpret graphical data used
CNN model ( Convolutional Neural Network )
Implemented NLP using the Natural Language
Toolkit(NLTK) in Python, establishing a
frameworkcapable of understanding and
validating engineering terminologies and
specifications
Ensuring the algorithms function effectively in real-time
Managing and interpreting the complexity and variability of
engineering drawings
Addressing variations in standards across different engineering
domains and geographical locations
Ensuring precise OCR extraction, especially with handwritten or
faded text
Developed adaptive algorithms capable of learning and
improving interpretation skills from continual data input and
feedback
Implemented a modular approach, allowing for the easy
integration of various standardization modules applicable to
different domains and regions.
Utilized data augmentation and ensemble learning to enhance
OCR accuracy, and introduced a feedback loop for continuous
model improvement
Techstack

7. 3) AI-POWERED WHATSAPP AND FAQ CHATBOTS FOR EDUCATIONAL
INSTITUTIONS
FAQ chatbot
Designed to guide users through
website navigation, providing instant
answersto common inquiries and
facilitating a user-friendly
experience.The FAQ bot was trained
on a myriad of common queries and
their respective answers, ensuring it
could accurately respond to user
inquiries and guide them through the
website effectively
Communication
Through the adept integration of artificial
intelligence, machine learning, and chatbot
technologies, the project has successfully
augmented the communicational capacities
of educational institutions. Both the WhatsApp
and FAQ chatbots have not only streamlined
informational dissemination and inquiry
resolution but have also enriched the user
experience for students, parents, and staff, by
providing instant, accurate, and personalized
interactions
This project spotlighted the creation of intelligent, AI-driven chatbots, specifically designed for WhatsApp and
institutional FAQs, aimed to facilitate seamless interaction between educational institutions and their diverse
stakeholders, including students, parents, and staff.
WhatsApp chatbot
Engineered to facilitate real-time
communicationvia WhatsApp,
answering queries, sending
notifications, and providing
assistance 24/7. The chatbot
discerns user inquiries through
NLP, and based on identified
intents, fetches relevant
information or performs
designated actions, such as
sending notifications or providing
real-time updates

8. Ensuring the algorithms function effectively in real-time
Challenges Solutions
Utilized the WhatsApp Business API for
integration, Python for backend
development, and Dialog flow for natural
language processing and intent recognition .
Techstack
Leveraged Rasa for NLP and chatbot
development, integrated with website
frontend technologies like HTML, CSS, and
JavaScript
Catering to inquiries in various languages to ensure inclusivity
Incorporated multilingual NLP models, allowing the chatbots to
understand and respond in multiple languages
Ensuring the system can manage peak times of user interaction,
especially during admission or exam seasons
Adopted cloud-based solutions, allowing the system to scale
resources as per user traffic, ensuring smooth functionality
Ensuring user data is protected and utilized in compliance with
data protection regulations
Implemented robust encryption and compliance protocols, and
ensured the chatbots operated transparently concerning data
usage and storage.

9. 4) VOICE-ACTIVATED TRAFFIC MONITORING AND ANALYSIS APPLICATION
USING ALEXA AND IBM WATSON
Analytical reporting
Analyzed live traffic feeds to
calculate and reportvehicular
counts and categories. The
camera feeds were analyzed
using IBM Watson’s visual
recognition, which parsed
through the visuals and identified
different vehicular entities,
categorizing and countingthem
accordingly
Backend systems
Developed a robust backend
systemand implemented Power
BI reportsthroughcloud
functions.Analyzed data was
stored in the Azure SQL
Database, and Power BI
leveraged this data to generate
insightful reports,
which were made accessible
through Azure cloud functions
This project aims at bridging this gap by developing a voice-activated, AI-driven application that harnesses the power of
Alexa and IBM Watson
Real-time monitoring
Enabled users to accesstraffic
camera feedsby vocalizing
street names to Alexa.
Developedcustom Alexa skills
to interpret user voice
commands, thereby triggering
appropriate AWS Lambda
functionsto fetch relevant
camera feeds from the backend,
powered byIBM Watson

10. Challenges Solutions
Ensuring the algorithms function effectively in real-time
Ensuring seamless processing of voluminous real-time data.
Ensuring the precise categorization and counting of vehicles
Safeguarding user data and ensuring compliance with data
protection regulations
Employed IBM Cloud Functions to ensure scalable, serverless
operations, ensuring consistent performance even under high
data loads
Continuously trained IBM Watson using diverse vehicular visuals
to enhance accuracy in real-time recognition and analysis
Implemented end-to-end encryption and adhered to GDPR and
other relevant data protection frameworks
EmployedPower BIfor creatinganalytical
reports, Azure Cloud Functionsfor
backend logic, and Azure SQL Databasefor
storing analyzed data
UtilizedIBM Watson Visual Recognition to
analyze camera feeds, IBM Cloud Functions
for serverless operations, and IBM Db2for
database management
Utilized Alexa Skills Kit (ASK)for voice user
interface development, integrated with IBM
Watsonthrough AWS Lambda functions
Techstack

11. 5) INTEGRATED ENVIRONMENTAL MONITORING SYSTEM USING MQTT,
PYTHON, SQL ALCHEMY, REACT, AND NEXT.JS
Testing
Each type of sensor(MQ-135, DS18B20, DHT22)
was calibratedin controlled environments to
ensure accuracy. The MQ-135 sensors were
calibrated using known concentrations of various
gasesWe conducted thorough testingto ensure
seamlessintegrationbetween the sensors, MQTT
messaging, backend processing, database
management, and frontend display. The backend,
especially the FastAPI component, underwent
rigorous load testingto ensure it could handle
high volumesof incoming data without latency or
data loss. The React and Next.jsfrontend was
tested across different devices and browsers for
compatibility, responsiveness, and user
experience
Validation
The system was deployed in a small
industrial areafor real-world testing.
It successfully monitored and reported air
quality, temperature, and humidityover
several weeks, demonstrating reliability
and accuracy
User feedback was collected to refine the
interface and functionality. This feedback led
to several iterative improvementsin the
system
The project aims to develop an integrated environmental monitoring system capable of real-time tracking of air quality,
temperature, and humidity
Architecture
The system architecture is designed to
maximize efficiency and reliability.
Sensors for air quality (MQ-135),
temperature(DS18B20), and humidity
(DHT22)are deployed in the field.
These sensors send their readings to a
central server via MQTT, ensuring timely
data transmission.
The server, powered byPythonand
FastAPI, receives this data and processes it
in real-time.
Data is stored and managed in a
PostgreSQLdatabase through SQL
Alchemy, ensuring data integrity and ease
of access

12. Challenges Solutions
Python’s versatilityand FastAPI’sspeed make for a
powerful combination in handling backend operations.
FastAPI facilitates quick development of endpoints for
data reception and processing, with Python ensuring
broad compatibility with various libraries and tools
MQTTplays a crucial role in ensuring efficient
and real-time datatransmission from
environmental sensorsto the server
Ensuring the algorithms function effectively in real-time
Initially, handling large volumes of real-time data from multiple
sensors without lag was challenging.
As the amount of collected data increased, we faced challenges
in database scalability and query performance
Early user feedback indicated that the data presentation on the
frontend was not as intuitive as desired
Optimizing the MQTT broker configuration and implementing
asynchronous processing in FastAPI
SQL Alchemy queries were optimized and introduced database
indexing. Migration to robust database system for future
scalebility
We revamped the frontend design using React and Next.js,
focusing on a more user-friendly interface, clearer graphs, real-
time updates, and a more interactive design
Techstack
As an ORM library, SQL Alchemyabstracts database
operations, providing a flexible and efficient way to
manage data storage. This project uses SQL Alchemy to
interact with a PostgreSQL database, chosen for its
robustness and scalability