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With the ever-changing technical aspects and shifts in the IT scapes, it is vital to keep ourselves on toes. Keeping this in mind, our latest projects dealt with leading technologies like AI, Python, IoT, PHP, Software Testing, etc.
Aeroponic has several advantages over traditional agriculture, aimed to improve the efficiency and environmental impact of agriculture. This technique contains monitoring and automation for proper operation. Automatic monitoring aeroponic-irrigation systems are based on IoT and Arduino. Analog and Digital sensors for measuring the temperature, humidity, pressure, pH, water flow and level of a nutrient solution. Meanwhile, the control system was used to manage actuators. Sensor’s data are transmitted via the internet into servers in order to facilitate easier monitoring for users. The prototype of the system is successfully implemented and provides a sensor’s data.
Modern smart street lights are energy-efficient, utilizing light sensors to illuminate roads at night and automatically switch off in the morning. This automation aims to reduce power consumption when there's no vehicle activity. Automation, driven by technology, simplifies tasks and reduces the need for human labor in various industries. It plays a crucial role in the global economy and daily life. Automatic street lights, employing IR sensors, save energy by lighting up a block of lights as a vehicle approaches, turning them off as it passes. This innovation helps conserve a significant amount of energy, keeping lights off when no vehicles are on the road
A turf playground serves various sports like football, rugby, tennis, cricket, etc. Popular for safety and vibrancy, schools and clubs prefer it for practice. Booking can be challenging due to timing. This website streamlines bookings with Admin, Manager, and User modules. Admin manages locations, assigns managers, sets prices, and monitors bookings. Managers handle requests, approve bookings, generate bills, and oversee history for specific locations. Users check availability, provide personal details, make payments, and review past bookings.
Crop disease diagnosis in agriculture research is crucial. Distinguishing fine-grained crop diseases is essential, as treatment methods vary. We use Image Processing and deep learning to create a system for accurate crop disease identification. Our model, MDFC-ResNet, works across species, coarse-grained, and fine-grained diseases. It incorporates a compensation layer to fuse multidimensional recognition results, outperforming other deep learning models in practical agricultural use.
A club management system project that provides and manages various club activities such as member registration, registration for various regular and vacation batches and more. The sports club management system software is a .NET built system that manages the entire club activities and provides respective functionality for various types of visitors. This system is built with respect to managing a cricket club. It allows normal users to avail for club membership, book the ground at for desired days and even enroll for various activities in the club. The sports club management system is built keeping in mind various various daily activities of a cricket club and the software automates all these club functionality for easy operation of the club.
This project aims to develop an efficient online invoicing application using Django, a Python web framework, following the Software as a Service model. It streamlines financial transactions, offers cost savings, and enhances business operations, emphasizing modern technology for tax transparency. Built with Django for the back-end and HTML/CSS for the front-end, the system provides a comprehensive user interface for generating bills and managing customer information. It also supports saving invoices in PDF format, offering businesses of all sizes a valuable tool for simplifying invoicing and improving financial visibility.
Study and Analysis of Implementing a Smart Attendance Management System Based on Face Recognition Technique using OpenCV. We can make our Attendance Management System (AMS) intelligent by using a face-to-face recognition strategy. For that, we have to fix a CCTV camera in the classroom at any point, which makes a person's picture at a fixed time and tests a face-to-face image. Traditionally, student attendance at the institutes is manually reported on the attendance sheets. It's not a productive operation, because it takes 5 or more minutes for attendance. Normally, the length of our class is 50 minutes, and every day we have more than 5 lessons. So, both courses take more than 50 minutes, which is almost the same as our class time. To solve this big issue we are proposing a novel automatic technique namely "Face Detection with OpenCV".
In order to achieve efficient and accurate breast tumor recognition and diagnosis, this paper proposes a breast tumor ultrasound image segmentation method based on U-Net framework, combined with residual block and attention mechanism. In this method, the residual block is introduced into U-Net network for improvement to avoid the degradation of model performance caused by the gradient disappearance and reduce the training difficulty of deep network. At the same time, considering the features of spatial and channel attention, a fusion attention mechanism is proposed to be introduced into the image analysis model to improve the ability to obtain the feature information of ultrasound images and realize the accurate recognition and extraction of breast tumors. The experimental results show that the Dice index value of the proposed method can reach 0.921, which shows excellent image segmentation performance.
Overweight and obesity pose public health concerns, linked to disease risks, morbidity, and mortality. This study employs machine learning for predictive modeling of obesity or overweight based on physical condition and eating habits data. Various algorithms were tested, with the best performer, random forest, achieving 78% accuracy, 79% precision, 78% recall, and 78% F1-score. This research underscores the potential of machine learning in identifying individuals at risk and aiding healthcare decision-making.
