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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 aim of our project is to develop and deploy a secure mobile banking app using QR image. In this project we have two parts a mobile app part and a website part. The client service application will be an Android application which can be installed on any smart phones. When the user downloads the application, he has to login for the first time to sink with the bank server. After sinking, user can perform mobile banking. More security is ensured in this application. The web part of this application is the bank server who works as an administrator. The functionalities of admin is to add new customers ie; their personal details, account details and security details. Administrator can change his password also. Different mobile banking applications are available now a day. But security is the main issue in using mobile banking applications. Anyone can download the application and use it, if they know the id and password. In order to improve security QR code is embedded on color image. Hence QR image is also needed along with customer id and password to enter into a mobile transaction or banking
The project is to be implemented for public buses (for ex: PMTs in Pune). It has the entire smart assistance system required for public security and safety. The smart system includes safety for women as well. It has an accident detection and monitoring facility. It also has a user-friendly application for users to track bus on smart their phones. The smart system can be designed for both online (GPS) and offline (GSM) for user friendly service. Here, a GPS system is used to get real-time co-ordinates for offline (GSM) system. The system also has many additions feature to make public transport system an intelligent and easy to use system, so that public can take smart advantage of it. The system is specially designed for Smart Cities as it is trending now-a-days.
Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labelled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this project, an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. It is worth noticing that applying image enhancement approaches improve the performance of the proposed method.
The quantity and generation rate of solid waste will continuously increase tremendously as long as human activities continue to exist. The consequences are caused by bad management of solid waste, it is very dangerous to the world and human health. This research developed a solid waste collection management model with the aim to design a Mobile Application model for solid waste collection management which will help to improve the quality of management in Solid Waste collection and impacting positively the environment by reducing the quantity of Solid waste. It provided all necessary and recent mobile technology and a model can be applied to achieve an efficient solid waste collection system for households.
A graphical password system with a supportive sound signature to increase the remembrance of the password is discussed. In this system a password consists of sequence of some images in which user can select one click-point per image. In addition user is asked to select a sound signature corresponding to each click point this sound signature will be used to help the user in recalling the click point on an image. Users preferred CCP to Pass Points, saying that selecting and remembering only one point per image was easier and sound signature helps considerably in recalling the click points.
A new method is proposed for the region of interest (ROI) extraction using fingertips and finger valley key points. Some new features and a new classifier are proposed based on information set theory. Information set stems from a fuzzy set representing the uncertainty in its attribute / information source values using the information-theoretic entropy function. The new feature types include vein effective information (VEI), vein energy feature (VEF), vein sigmoid feature (VSF), Shannon transform feature(STF) and composite transform Feature (CTF). A classifier called the improved Han man classifier (IHC) is formulated from training and test feature vectors using Frank t-norm and the entropy function. The performance and robustness are evaluated on GPDS and BOSPHORUS palm dorsal vein database under both the constrained and unconstrained conditions.
Data hiding conceals data within cover media, linking two datasets: embedded data and cover media data. In covert comms, hidden data is often irrelevant, while in authentication, it's closely related. Invisibility is key. Sometimes, data hiding causes irreversible distortion in cover media. In remote sensing and high-energy particle experiments, reversible, lossless, and distortion-free techniques are vital. Reversible data hiding allows message embedding in distortion-free media, like military or medical images, ensuring perfect content restoration. Encryption transforms data into unintelligible content for privacy, typically applied before processing or after decryption.
Crop disease diagnosis is crucial for effective treatment and a pressing concern in agriculture. Identifying precise disease grades is vital, as treatments vary. We've developed an Image Processing and deep learning system, MDFC–ResNet, which detects and diagnoses crop diseases accurately across species, coarse-grained, and fine-grained levels. This innovative system outperforms other deep learning models in real-world agricultural applications.The HDFC–ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.
Cloud services, vital in private, public, and commercial sectors, demand unwavering security and resilience. This paper introduces online cloud anomaly detection, emphasizing one-class SVMs at the hypervisor level, showcasing high detection accuracy exceeding 90% against malware and DoS attacks, while highlighting the importance of system and network data in versatile detection. This approach, involving dedicated monitoring components per VM, adapts adeptly to cloud scenarios, even with unknown malware strains.
In the era of social networking and real-time communication, the vast amount of textual data generated on comment services presents a unique challenge and opportunity for information retrieval and consumption. "IncreSTS Text Summarization" represents an innovative approach to real-time text summarization in the context of social networks and comment services. This research explores methods and techniques to extract key information, trends, and sentiments from the dynamic and rapidly evolving landscape of comments and discussions on social media platforms. By harnessing the power of text summarization, this work aims to enable users to efficiently digest and engage with the wealth of user-generated content on these platforms. The "IncreSTS Text Summarization" project offers a promising avenue for enhancing the accessibility and utility of real-time commentary in the digital age.
