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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.
The Blind e-commerce app is a groundbreaking mobile application designed to empower visually impaired individuals by facilitating independent online grocery shopping. Leveraging text-to-speech and voice recognition technologies, the app offers a user-friendly interface for streamlined product search, easy navigation, secure payments, and order tracking. By eliminating the need for external assistance, this app has the potential to significantly enhance the quality of life and independence of visually impaired users, revolutionizing their shopping experience and daily tasks.
In the current complex legal landscape, the Law System Project bridges the gap between individuals and the legal system, offering easily accessible and comprehensible legal guidance. By incorporating the Indian Penal Code (IPC) and Civil sections into a comprehensive database, the project equips users with vital information to navigate the legal system. Users can also report incidents, providing details like time, date, location, and involved parties, streamlining the process of seeking justice. This user-friendly platform empowers individuals to make informed decisions and take charge of their legal matters, contributing to a more just and equitable society where legal guidance is accessible to all.
The Tech News App is a specialized Android platform offering the latest technology news, insights, and analysis. Tailored for tech enthusiasts and professionals, it covers a wide range of topics, including smartphones, software, artificial intelligence, and more. Unlike general news apps, it delivers a focused tech news experience. Admin oversight ensures content accuracy and relevance. Users enjoy personalized news feeds, fostering a sense of community. In a rapidly evolving tech landscape, the Tech News App is the essential tool to stay updated. With its niche focus, content control, and community engagement, it remains a reliable source for the latest in tech.
MotoAssistant, an Android app, offers on-road vehicle breakdown assistance and a platform for sellers to list vehicle-related products. It has four main modules: ADMIN, USER, WORKSHOP, and SELLER. The app's user-friendly design simplifies service requests and product purchases.
Underwater images often suffer degradation due to factors like light scattering, absorption, and reflection, causing reduced visibility and color distortion. To enhance image quality, It introduces a two-stage dehazing method. estimate the transmission map using the dark channel prior, effectively measuring haze thickness. Then, color correction is applied to restore color balance. By employing a color transfer function and selecting a reference image based on content and lighting similarity, it improve the visual quality and quantitative metrics of contrast and colorfulness in underwater image dehazing, outperforming existing methods.
A method for diabetic retinopathy detection through image processing. Retinal images are enhanced, relevant features extracted via image processing, and a machine learning algorithm trained for classification. The system's potential benefits include enhanced accuracy and accessibility in healthcare.
A novel technique creates a mosaic image from a secret image, making it appear like a chosen target image. Skilled color transformation ensures nearly lossless secret image recovery. Overflows/underflows are managed by recording color differences in the original space. Embedded information allows lossless secret image retrieval, validated through successful experiments.
A web-based blue-collar job management system streamlines scheduling, task assignment, and tracking. It employs machine learning and optimization to enhance efficiency, considering worker availability, location, and skills. This system boosts productivity and communication, benefiting the economy and society.
Deep learning-based chest CT analysis is efficient for COVID-19 diagnosis, but large labeled datasets are scarce. We propose ResNext+, a weakly-supervised approach using volume-level labels, lung segmentation, spatial features, LSTM, and slice attention for slice-level predictions.It shows an 81.9% precision and 81.4% F1 score, which can be further enhanced with image enhancement techniques.
