Author Archive: Yugesh Verma

Garments ERP – Apparel & Textile Industrial ERP Software

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Welcome to the world of Garments ERP , where precision meets efficiency in the garments and apparel industry. Our tailored Enterprise Resource Planning software is your key to streamlining and optimizing every facet of clothing and textile management.

Our Garments ERP keeps you in control by offering real-time insights into raw materials, fabric management, production progress, and finished products. Say goodbye to inefficiencies and waste, and welcome efficient quality management.

It aids in planning production schedules, resource allocation, and order monitoring, leading to optimized production processes and reduced lead times. Your operations become smoother, more efficient, and more cost-effective.

With Garments ERP, you can process buyer orders with ease, from entry to fulfillment and delivery. It ensures accuracy and punctuality, satisfying your customers and bolstering your market reputation. Efficient supplier management guarantees a steady supply chain, reducing production delays and boosting your competitive edge.

Our software doesn’t stop at production – it’s equipped with financial modules that manage accounts payable, receivable, cost tracking, and budget management. It empowers you to make informed financial decisions and generates reports to aid in your strategic planning.

Garments ERP offers apparel businesses heightened operational efficiency, reduced costs, greater customer satisfaction, and sharper decision-making capabilities. It’s your essential tool for thriving in the fast-paced and ever-evolving garment industry. Step into the future with Garments ERP and see the difference for yourself.”

What You Get :

Key Features:

Dashboard
  • Access real-time company data from the dashboard.
Order Management
  • Create orders for buyers, including Booking, Budget, Costing, Sample, Production, and Shipments.
Manage Inventory
  • Create Accessory Units and Accessories.
  • Place accessory orders with suppliers.
User Management
  • Administer user accounts and assign roles.
Bank & Accounts
  • Conduct financial transactions.
  • Access Bank Accounts, Cash in Hand, Cheques, Income, Expense, Credit Voucher, Debit Voucher, Monthly Transactions, Daily Transactions, Party Ledger, and Daily Cashbook.
Party List
  • Create and manage two types of parties: Suppliers and Buyers.
  • Purchase accessories from Suppliers.
  • Sell orders to Buyers.
HRM Management
  • Define designations and manage employee data.
  • Handle full or partial salary payments.
Party Due List
  • Monitor dues for parties (Suppliers and Buyers).
  • Process payments to Suppliers.
  • Receive payments from Buyers.
Loss Profit
  • Analyze the company’s financial situation.
  • Review expenses, incomes, and the overall Loss/Profit.
Reports
  • Generate Order, Transactions, Production, Sales, and Purchase Reports.
Settings
  • Configure Currency settings.
  • Manage Notifications.
  • Update core system settings.
  • Adjust website settings.
Roles, Permissions & Others
  • Control user visibility and permissions.

For Live Demo & Enquiry  :

WhatsApps : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included

Lung Cancer Detection Using CNN Project with Source Code

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Lung cancer is one of the most prevalent and deadly forms of cancer worldwide. According to the World Health Organization (WHO), lung cancer accounts for approximately 2.1 million new cases annually and is responsible for 1.8 million deaths. The high mortality rate is largely due to the fact that lung cancer is often detected at an advanced stage when treatment options are limited, and survival rates are low. Early detection of lung cancer significantly improves the chances of successful treatment and long-term survival. However, traditional diagnostic methods, such as X-rays, CT scans, and biopsies, are time-consuming, require significant expertise, and are not always effective at detecting early-stage tumors.

This project aims to detect lung cancer using a Convolutional Neural Network (CNN) model deployed with Flask. It includes a Jupyter notebook (lung_cancer_detection.ipynb) for model training and a Flask app (app.py) for making predictions. Additionally, an HTML template (index.html) is provided for the web interface.

Data Preprocessing

  • Data Acquisition: The CT-Scan  images and their labels were obtained from this Kaggle dataset. It provides a diverse set of chest cancer images, crucial for training a robust model.

  • Data Augmentation: To enhance the model's ability to generalize and to mitigate overfitting, I used TensorFlow's ImageDataGenerator. This tool allowed for augmenting the images in various ways (like rotation, zoom, flip) to artificially expand the dataset.

  • Normalization and Resizing: Each image was resized to a standard dimension and normalized to ensure uniformity in the input data, which is important for effective training of the CNN.

  • Train-Test Split: The dataset was split into training and test sets. The training set is used to train the model, while the test set helps in evaluating its performance on unseen data.

Model Structure

The CNN model used in this project is a sequential model composed of several Convolution2D and MaxPooling2D layers, carefully structured for effective image classification:

  • Convolution Layers: The model includes multiple Conv2D layers with 128 and 256 filters. These layers are responsible for extracting features from the images. The first two convolution layers have 128 filters each, followed by another set of two with the same number. The next four convolution layers have 256 filters each.

  • Pooling Layers: MaxPooling2D layers are used after certain convolution layers to reduce the spatial dimensions (width and height) of the output volume, helping to reduce the number of parameters and computation in the network.

  • Output Layer: The final layer of the model is a Dense layer with neurons equal to the number of classes, using a 'softmax' activation function for multi-class classification. This allows the model to output a probability distribution over the classes.

  • Optimization and Loss Function: The model is compiled with the Adamax optimizer and categorical cross-entropy loss function. This combination is chosen for effective learning and generalization in multi-class classification tasks.

  • Parameters and Size: The total number of parameters in the model is 3,763,940 (14.36 MB). All these parameters are trainable, ensuring that the model can learn complex patterns in the data.

This structure is designed to effectively capture the intricate patterns in CT-Scan images, leading to accurate classification of Lung Cancer.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Brain Tumour Detection Project Using Deep Learning From MRI Image

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Brain tumors are one of the most critical and life-threatening conditions affecting the human brain. Early and accurate detection of brain tumors is crucial for effective treatment planning and improving patient outcomes. However, manual detection and diagnosis from MRI (Magnetic Resonance Imaging) scans can be time-consuming, prone to human error, and require expert radiologists.

This project focuses on automating the process of brain tumor detection using deep learning techniques. By leveraging the power of Convolutional Neural Networks (CNNs), we aim to create a model that can accurately classify MRI images into tumor and non-tumor categories. The trained model is then integrated into a user-friendly web application developed using Flask, allowing healthcare professionals and researchers to easily upload MRI images and receive real-time predictions.

Data Preprocessing

  • Data Acquisition: The MRI images and their labels were obtained from this Kaggle dataset. It provides a diverse set of brain images, crucial for training a robust model.

  • Data Augmentation: To enhance the model's ability to generalize and to mitigate overfitting, I used TensorFlow's ImageDataGenerator. This tool allowed for augmenting the images in various ways (like rotation, zoom, flip) to artificially expand the dataset.

  • Normalization and Resizing: Each image was resized to a standard dimension and normalized to ensure uniformity in the input data, which is important for effective training of the CNN.

  • Train-Test Split: The dataset was split into training and test sets. The training set is used to train the model, while the test set helps in evaluating its performance on unseen data.

Model Structure

The CNN model used in this project is a sequential model composed of several Convolution2D and MaxPooling2D layers, carefully structured for effective image classification:

  • Convolution Layers: The model includes multiple Conv2D layers with 128 and 256 filters. These layers are responsible for extracting features from the images. The first two convolution layers have 128 filters each, followed by another set of two with the same number. The next four convolution layers have 256 filters each.

  • Pooling Layers: MaxPooling2D layers are used after certain convolution layers to reduce the spatial dimensions (width and height) of the output volume, helping to reduce the number of parameters and computation in the network.

  • Output Layer: The final layer of the model is a Dense layer with neurons equal to the number of classes, using a 'softmax' activation function for multi-class classification. This allows the model to output a probability distribution over the classes.

  • Optimization and Loss Function: The model is compiled with the Adamax optimizer and categorical cross-entropy loss function. This combination is chosen for effective learning and generalization in multi-class classification tasks.

  • Parameters and Size: The total number of parameters in the model is 3,763,940 (14.36 MB). All these parameters are trainable, ensuring that the model can learn complex patterns in the data.

This structure is designed to effectively capture the intricate patterns in MRI brain images, leading to accurate classification of brain tumors.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Buy Now Source Code ₹1501

Sleep Disorder Prediction Using Machine Learning Project

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The purpose of this project is to analyze factors that affect sleep health and predict which sleep disorder a person may have based on various factors such as Sleep Quality, Stress Level, BMI Index, Blood Pressure, etc. The dataset used for this project was sourced from Kaggle. The project involved data preprocessing to fill null values, exploratory data analysis (EDA), and the implementation of machine learning models to make predictions.

Key Observations

  1. Individuals aged over 43 years are more likely to experience sleep disorders.
  2. On average, females tend to have better sleep quality compared to males.
  3. Engineers generally have better sleep quality, while Sales Representatives have poorer sleep quality.
  4. Higher stress levels are associated with an increased likelihood of sleep disorders.
  5. Individuals with sleep disorders have lower sleep quality ratings compared to those without disorders.
  6. People in the Obese and Overweight BMI categories tend to experience more sleep disorders.
  7. Individuals who sleep more than 7 hours have a significantly lower chance of having a sleep disorder.

Models Used

Three machine learning models were used:

  1. Logistic Classification
  2. K-Nearest Neighbors (KNN) Classifier
  3. Random Forest Classifier

The same training and testing sets were used for all models.

Model Performance

  • Random Forest Classifier: Best performing model with an accuracy of 89%.
    • Accuracy: 89%
    • Recall: 89%
    • Precision: 90%
    • F1-Score: 89%
  • KNN Classifier: Performed well but not as good as Random Forest.
  • Logistic Classification: Achieved 86% accuracy, which was the lowest among the three models.

Important Features

Based on the Random Forest Classifier, the top three important features for detecting sleep disorders are:

  1. Blood Pressure
  2. BMI Category
  3. Age

Conclusion

 

The Random Forest Classifier was identified as the best model for predicting sleep disorders in the given dataset, achieving an accuracy of 89%. The top three contributing features to the prediction are Blood Pressure, BMI Category, and Age.

Dataset

The dataset for this project was obtained from Kaggle.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Buy Now Source Code ₹1001

 

Oral Cancer Detection Using Image Processing with Source code

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Oral cancer is cancer that develops in the tissues of the mouth or throat. It belongs to a larger group of cancers called head and neck cancers. Most develop in the squamous cells found in your mouth, tongue, and lips.

Oral cancer is a major health problem worldwide accounting for 177,384 deaths in 2018 and is most prevalent in low- and middle-income countries. Enabling automation in the identification of oral cancer can lead to the prevention and early diagnostic of disease. Therefore, regular oral check-ups are very important. The focus of transfer learning is to enhance the performance of target learners on target domains by inheriting knowledge from various conceptually related source domain. This project implies a novel approach for the early diagnosis and detection of one of the leading diseases, cancer in most sensory body organ i.e., mouth. In addition to this, deep neural networks are used to build automated systems, where complex patterns are derived to track with this difficult task. Various Transfer Learning architectures has been improvised and comparative analysis has been derived to focus the best learning rate. Further analysis is reported in relation to the classification of the referral decision. Our introductory results shows that deep learning has the power to tackle this challenging task.

In the enlargement of cells that provide with harmed nearby tissues, oral cancer has been documented to be intractable [1]. Oral cancer can be detected in the early stages of mouth cancer, known as ulcers, by a tiny number of sterile cells in the oral tissue. When it comes to metabolism, dead cells can be found in remote places of the geographical region or inside the body. There are several types of cancer, however 90 percent of crab cells are classified as oral cancer [2]. Biological models, as well as clinical forms of related and lesion-free tumour models, can be detected in various parts of the body using appearance models and stereotypes that do not require staining. Machine learning techniques were utilised to predict several biological models for oral cancer, which were used to classify non-cancerous and cancerous samples, which were then analysed for the oral cancer stage [3]. The predictor will use three justification test kits as well as different stages of cancer to determine the accuracy of the interaction. Sampling with sample justification can predict different tumour volumes and the emergence of ulcers in the tissues, assisting in the prediction of different stages of oral cancer [4].

Without the need of any special instruments, the oral cavity can be easily viewed. During clinical practise, specialists use visual inspection to make suspected diagnoses of oral cancer based on their own knowledge and experience with the visual appearances of malignant tumours [5,6]. Oral cancer lesions typically appear as white patches followed by red patches, or as mixed white-red patches in rare cases. The mucosal surface frequently becomes progressively uneven, grainy, and ulcerated [7,8]. Non-specialist medical workers, however, sometimes misinterpret these visual patterns as indicators of ulceration or other types of oral mucous membrane illnesses [8]. For the detection of oral cancer, there is no established vision based technique. Oral cancer is diagnosed through oral biopsy, which takes a long time and is not always available in primary care or community settings, especially in developing countries [9, 10]. As a result, Oral Cancer patiens are frequently unable to acquire prompt diagnosis and referrals [11,12].

There is a lot of evidence that Deep learning algorithms can match, and in some cases outperform, human experts when it comes to identifying minute or miniscule visual patterns from photographs,[13], classifying skin lesions, [14], detecting diabetic retinopathy, [15], and identifying facial phenotypes of genetic disorders [16]. These findings lead us to anticipate that deep learning could capture fine-grained aspects of oral cancer lesions, which would be useful in the early diagnosis of the disease.

The concept of transfer learning is founded on the premise that when knowledge or information from a related domain is transferred to it, it improves an idea in that domain. Consider the case of two people who want to learn to play the flute. One of the participants has no prior musical experience, while the other has a strong understanding of music as a result of playing the sitar. By applying previously learned music knowledge to the process of learning to play the flute, a person with a good music background will be able to learn the flute more quickly [17].

A deep learning system was built using photographic images for entirely automated oral cancer detection when it was assumed that deep neural networks could quickly identify certain visual patterns of oral cancer just like any human expert. On both internal and external validation datasets, we calculated algorithmic performance and compared the model to the average result of seven oral cancer specialists on a clinical validation dataset. Our findings demonstrated that oral cancer lesions have discriminative visual patterns that can be discovered using a deep learning algorithm. The potential to identify oral cancer at the point of care in a less expensive, non-invasive, and effective method has substantial clinical implications.

Datasets Download Link - Kaggle 

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.8, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

How to Download and Install JDK, Netbeans 8.2 in Windows 10

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Netbeans 8.2 installation 💻 How to install Netbeans 8.2 in Windows 10/11

NetBeans IDE lets you quickly and easily develop Java desktop, mobile, and web applications, as well as HTML5 applications with HTML, JavaScript, and CSS. Batch analyzers and converters are provided to search through multiple applications at the same time, matching patterns for conversion to new Java 8 language constructs. The IDE also provides a great set of tools for PHP and C/C developers.

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Online Multi Vendor Bus Ticket Booking App Flutter Solution

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Embark on a seamless travel experience with ProZigzagBus, the ultimate online multi-vendor bus ticket booking app and reservation system powered by Flutter.

Our innovative solution provides a user-friendly platform for effortless ticket booking and reservations across a diverse range of bus vendors. Explore the convenience of choosing from multiple vendors, ensuring flexibility and choice for every journey.

With ProZigzagBus, travel meets technology, offering a secure and efficient reservation process. Elevate your travel adventures with confidence and ease. Simplify your journey with ProZigzagBus – where multi-vendor options converge for a travel experience like never before.

What’s included:

  • A complete fully working system.
  • Customer App (Android & iOS), Driver App (Android & iOS), & Agent (Android & iOS) Flutter code.
  • An Admin Panel & Operator / Vendor Panel (PHP Based)
  • Database(MySQL)
  • Constant development with regular updates.
  • Clean, well-structured and maintainable code
  • Active customer support to help.
  • Value for money system.
  • Flexible prices, no surprises!

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included

Car Rental Booking App Complete Solution

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is your ultimate solution for all your car rental needs. Whether you need a car for a quick ride around the city, a reliable taxi service, or the freedom of a self-drive adventure, CarLink has got you covered. Our user-friendly app makes renting a car effortless and convenient, putting the power of choice right at your fingertips.

Our seamless booking process allows you to reserve your car in just a few taps, saving you time and hassle. Plus, with our transparent pricing and flexible rental options, you can enjoy peace of mind knowing that you’re getting the best value for your money!

What’s included:

  1. A complete fully working system.
  2. Customer App (Android & iOS) Flutter code.
  3. An Admin Panel (PHP Based)
  4. Database(MySQL)
  5. Constant development with regular updates.
  6. Clean, well-structured and maintainable code
  7. Active customer support to help.
  8. Value for money system.
  9. Flexible prices, no surprises!

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included