To ensure patient safety and reduce the incidence of prescription errors, the healthcare industry places a high priority on the availability and accuracy of pharmaceutical information. MediCaption offers a unique solution to this issue with its integrated system, which combines the capabilities of computer vision driven with a state-of-the-art object detection model YOLO-v8 by Ultralytics [1], robust natural language processing (NLP), text-to-speech (TTS), and optical character recognition (OCR). This Project utilizes advanced AI and image processing to quickly and accurately annotate pharmaceutical packaging with key information like drug names, uses, and side effects, significantly reducing medication management errors and enhancing information precision and usability. Using a dataset of 372 pharmaceutical packages from Kaggle (Shah, 2021) [2], we annotated it with Roboflow and trained it using the YOLO-v8 model, achieving precise medicine name detection through bounding box accuracy. This enabled effective text extraction via OCR, following NLP preprocessing by matching against a medicinal database, allowed for the generation of informative captions. To improve user accessibility, these captions were subsequently translated into audio using Text-to-Speech (TTS) technology. This system is designed with computational efficiency and user accessibility in mind, making it beneficial for a wide array of users, including those with visual impairments.

Format has been changed for submitting it to IEEE LISS 2020 Conference.

This paper describes the author's vision in developing a flexible workflow infrastructure for enforcing the pragmatic interoperability in industries like manufacturing and healthcare. This vision is based on business continuity planning, web services interoperability, Node-Red and IFTTT workflow technologies.

In this research, we present a model architecture for employing reinforcement learning with transformers for multimodal tasks. Using transformers allows us to take advantage of the transformer architecture’s simplicity and scalability, as well as developments in language and vision modelling such as ViT, GPT-x, and BERT. Specifically we have trained the model to recognize various digits from the MNIST dataset from their associated word labels and instructions provided. The approach is similar to how an infant would learn to associate pictorial representation of digits to their corresponding words. We have used a MoME transformer in conjunction with Deep Q Learning to train our model. The image inputs have been embedded using pre-trained ResNet18 and the instructions have been embedded using GLoVe before passing them to the model for prediction and training.

Coronavirus (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most infected people will experience moderate to severe respiratory illnesses and recover without the need for specialized treatment. Some, on the other hand, will become quite ill and seek medical care. Older people and those with poor health conditions such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and get very sick or die at any age. Pneumonia is also an infection in one or both lungs caused by bacteria, viruses, or fungi. The infection leads to inflammation in the air sacs of the lungs, which are called alveoli. The alveoli get clogged with fluid or pus, making breathing difficult. So, for detecting the COVID-19 and Pneumonia in now a days become costly and time taking process especially for common people and radiologists. This paper introduces AUTO- ML process to detect the diseasesc(COVID or Pneumonia) inside the human lung.

Following image classification, the focus is now shifting to text categorization. Text classification has numerous real-world uses. Using categories to tag information or items to improve browsing or identify related stuff on your website. The practise of categorising text into ordered groupings is known as text classification, sometimes known as text tagging or text categorization. Text classifiers can automatically assess text and assign a set of pre-defined tags or categories depending on its content using Natural Language Processing (NLP). In this study, we will try to create a model that can detect abnormalities in a log data collection by combining NLP features with other methodologies. This is handled as a text categorization challenge. That is why we evaluate two of the most well-known techniques while also using extra features extracted from the data set. We shall contrast the bag of words technique with the embedding technique. As opposed to Bag of words, embedding tries to preserve the meaning of the sentence, which can aid with text classification.

This is a stage 1 of our ongoing research to build bedside machine learning predictors to identify septic shocks at early stages. It is an important research as the current COVID-19 causes such septic shocks and predicting them early save lives.

The given literature focuses on developing a Smart Factory model based on Cloud and Edge computing used to develop Transportation Management System(TMS) using a iFogSim wrapper. Cloud computing identifies data centres for users and offer computer system services on-demand, including data storage and processing power, without direct active user management. In the smart industry, several devices are connected together across the internet, where vast volumes of data are collected during the entire process of output. Thus, to handle this data smart factory based on cloud and edge computing is used. The intelligent cloud-based factory offers some facility like large scale analysis of data. Concepts like fog and edge computing play a significant role in extending data storage and network capacities in the cloud that addresses some challenges, such as over-full bandwidth and latency. The literature also focuses on the implementation of TMS using the iFogSim Simulator. The simulator provides efficient execution of TMS by showing the amount of resources used which gives an idea regarding optimum use of resources. All types of data related to TMS is obtained at cloud by using smart factory like object location, time taken and energy consumption. To implement the TMS we have created a topology which displays various devices connected to the cloud which gives necessary information regarding the ongoing transportation simulation.

This paper provides a comparative analysis between Deep Q Network (DQN) and Double Deep Q Network (DDQN) algorithms based on their hit rate, out of which DDQN proved to be better for Breakout game. DQN is chosen over Basic Q learning because it understands policy learning using its neural network which is good for complex environment and DDQN is chosen as it solves overestimation problem (agent always choses non-optimal action for any state just because it has maximum Q-value) occurring in basic Q-learning.

Social network analysis has been an essential topic with broad content sharing from social media. Defining the directed links in social media determine the flow of information and indicates the user's influence. Due to the enormous data and unstructured nature of sharing information, there are several challenges caused while handling data. Graph Analytics proves to be an essential tool for addressing problems such as building networks from unstructured data, inferring information from the system, and analyzing the community structure of a network. The proposed approach aims to determine the influencers on Twitter data, based on the follower's count as well as the retweet count. Several graph-based algorithms are implemented on the data collected to find the influencer as well as communities in the network.

Bank safety deposit box is one of the secure storage for the clients to store their valuables such as important property or business paper, gold or even money but is in some case scenario traditional safe deposit are not safe enough, there have been many robberies from this safe box because of the code lock can be hacked and there no alarming system in this kind of safe boxes. In this paper I proposed a smart secure safe box with facial reorganization unlock feature using raspberry pi and uno Arduino which can used to secure your documents or valuables in a bank or at your home. In this I have used raspberry pi and pi camera to perform the face unlock and to send security alert through email. Secondly, I have used Arduino and hall sensor to trigger an alarm when the door is open unauthorized or kept open.

Here I have mentioned the essential on the home IoT frameworks and difficulties related to them. After that, I have portrayed about essential requirements of the IoT gadgets and sensors. Additionally, I have referenced about the procedures and methodologies which I have utilized for key less door entry. The reader can get the clear idea regarding the venture by reviewing the wiring diagram and other screenshots.