From 2020 to 2023, SARS-CoV-2 destroyed much of our society, while few treatments were available due to the time required for drug discovery. However, with recent advancements in artificial intelligence, it is now ready to fight viruses such as SARS-CoV-2. Chemprop, a machine-learning backbone for molecular properties prediction, can be used to discover novel antiviral drugs by training a classifier model with hundreds of thousands of data points that include molecular information represented by SMILES strings and the observed efficacy in inhibiting SARS-CoV-2 in laboratory tests. The resulting model predicts the effectiveness of untested molecules, which then can be manually tested, minimizing tedious hunting traditionally done by human scientists. With promising performance, the proposed method pushes the boundary of machine learning’s involvement in drug research. The trained model achieved a high accuracy in predicting the effectiveness of drugs against SARS-CoV-2 with an AUC score of 0.8455. However, the model loses accuracy when predicting the effectiveness of drugs against SARS-CoV, a different strand of coronavirus, with an AUC of 0.7302. The model was then run on one of the data sets to locate the molecule most likely effective against COVID-19, demonstrating its applicability. The result was a molecule with SMILES string CN1CCN(CC1)C(=O)COC=2C=CC(C)=CC2 also called 1-(4-Methyl-piperazin-1-yl)-2-p-tolyloxy-ethanone. Then the model DrugChat was utilized to determine the properties of the molecule. The model’s ability to find likely drugs can hasten drug research drastically, potentially saving countless lives during future pandemics.

Traffic signs are a fundamental piece of transportation infrastructure and play a vital role in regulating traffic flow, enforcing proper driving behavior, and reducing the risk of accidents, injuries, and fatalities. An Intelligent Transportation System (ITS) must have the ability to automatically detect the sign and then recognise traffic signs which is to be effective. Automatic traffic sign detection is necessary. and is growing in significance with the advent of self-driving cars. This study introduces a brand-new deep learning-based method for identifying traffic signs in India. The proposed system utilizes a region-based convolutional neural network (CNN) to achieve automatic identification and recognition of traffic signs. The authors describe various architectural and data augmentation enhancements to the CNN model and take into account unique and challenging Indian traffic sign types that have not been previously discussed in literature. The system is trained and evaluated using a database of real-time images captured on Indian highways. The deep learning approach is utilized to work on the accuracy and precision of the system, determined to make automated driving automobiles.

Big data is a massive amount of information, measurements, and observations, where it has the power to provide a solution to the impossibilities. Recently, it has become the most trending topic in the field of data analysis because of its amazing potentials in extracting the hidden facts. Which attracted various sectors all over the world to collect and analyze the big data in order to improve their services and introduce high valuable products. Specifically, in the healthcare industry, different sources generate big data such as; hospital records, medical records of patients, and results of medical examinations. This type of data is related to the population healthcare, and it requires analysis in order to extract valuable knowledge. Nowadays, with the available high-end computing solutions for big data analysis. It becomes easy for researchers to have solutions that improve the healthcare level of the population. The promising thinking to give new technologies, high services, and big profits for healthcare, can revolutionize the medical solutions and help the community in overcoming the impossible cases. This research discusses essential clinical big data matters related to the healthcare sector by introducing a clear definition and features of the clinical big data in healthcare and its process. Also, by presenting analytics, applications, benefits, challenges, and future of the clinical big data technologies in the healthcare sector. This survey aims to review state of the art for the application of the clinical big data in the healthcare sector, in which it would be an apparent reference, where authors can refer to in their future research.

Deaf-and-dumb humans make up about 5% of the world's population, and they need special care by providing them alternative methods that help them to communicate with the outside world, whereas the sense of hearing is the main element of human communications, which is indispensable. From the standpoint of introducing helpful applications that help deaf-and-dumb population, the idea of this research aimed used deep learning techniques to create a model based on the principle of converting Arabic spoken digits to sign language images, through a study of two different datasets that were freely taken from open-source websites. The first one contains audio records of Arabic spoken digits that was used to train on-dimensional CNN model to generate a text translation of any Arabic spoken digit record. The second one contains sign language images of Arabic digits, where used to build IF-THEN rules system that can generate the sign language image as a translation of given Arabic digit text. The whole idea conducted through using both systems in one prediction model that can generate the sign language image of any giving spoken Arabic digits’ record, where it had accurate results with 86.85% accuracy value and 0.5039 loss value. The goal of this research is to add a new technology based on deep learning, in order to help this group of people with a simple idea that opens the researchers’ minds to produce a model of all Arabic spoken speech, which in turn can be a complete technology that helps deaf-and-dumb humans’ to easily communicate with the outside world.

Secure communication plays a vital role now-a-days. Modern-day secure communication is made possible via cryptography. Modern cryptographic algorithms are based on the process of factoring large integers into their primes, as they are intractable. But the cryptography nowadays is vulnerable to technological advances in computing power like quantum computing and evolution in math to quickly reverse one-way functions like factorization of large integers. Incorporating quantum physics concepts into cryptography is the answer, which results in an assessment of quantum cryptography. A unique type of cryptography known as quantum cryptography makes advantage of quantum mechanics to provide complete protection against the transmitted message. Quantum Key Distribution (QKD), a random binary key distribution used in quantum cryptography, enables communication participants to recognize unauthorized listeners. Quantum Key Distribution (QKD) is likely the most advanced quantum technology currently accessible with full stack systems already in use. This project’s goal is to develop secure and encrypted communication between the parties with the help of a web application using the BB84 protocol.

Using camera traps is common in animal studies. The camera is often activated when the movement is detected to prevent recording when nothing happens. It includes a collection of images of wildlife from Tanzania’s Serengeti National Park. Deep Learning is built on an understanding of the composition and it is the working of behaviour like CPU of the computer. Deep learning model is mainly working as the basic principle of neural networks to analyse any inputs like data or images and videos and make better accurate with predicted value with less loss percentage. With current systems, wind and sunshine may potentially move the plants and start recording, leading to a large number of blank images. Researchers will manually eliminate them from the study, which is a hard way of classification by manually and very much wastage of time. When there is a lot of data accessible, the system has all it needs to train itself. Deep residual neural networks, such as ResNet50, which are very helpful for object detection of many image data and make more viable to the conservation of wildlife are used in this proposed system. It aids in determining if the provided picture data is of an animal or not with better prediction, as well as training on a useful dataset like Serengeti2, where camera trap image collection yields accuracy of 94.64% with better prediction of tested data with greater precision and recall value.

This paper introduces a module which is used to transport goods or people from one place to another without any driver assistant. It is mainly used in big industries to save the time and energy. This module is built around an RFID sensor. RFID technology uses fields of electromagnetic waves to track and monitor tags attached to objects. When triggered with a field of electromagnetic waves investigate pulse from an adjacent RFID reader device, this tag delivers digital information back to the reader, which is often an inventory number. This number can be used to keep track of inventories. The sensors collect the data and sends to the main algorithm and then it takes the decision which way to go, we implemented our car to be completely automated and does not required any instructions from human. To achieve this we have chosen the shortest path algorithm know as Dijkstra's algorithm.

This paper introduces a groundbreaking automated forecasting process for global currency transactions, specifically designed for e-commerce and fintech companies. Traditional linear models, such as weekly moving averages, ARIMA, and SARIMA, have proven inadequate in capturing non-linearities and complex patterns within the data. To address these limitations, we propose an ensemble of diverse machine learning models. These models, characterized by varying lag periods, integrate regional holiday data, macroeconomic variables, and time-based variables. The proposed process exhibits high scalability, capable of simultaneously predicting forex currency transactions for multiple currencies. The implementation of this forecasting process empowers companies to manage currency exchange risk more effectively, enhance overall financial performance, and increase profits through consolidated transactions. Additionally, the automation of this process eliminates the need for manual forecasting, thereby boosting efficiency, accuracy, and employee morale. The findings of this study carry significant implications for the global e-commerce and fintech companies with operations in multiple currencies. They demonstrate the transformative potential of machine learning models in revolutionizing currency transaction forecasting and assisting in strategic decision-making for finance and treasury teams.

Our project's topic is algorithms for game design, which mainly describes a series of algorithms in the pygame library in Python, such as collisions, mazes, and a series of algorithms with different functions. The following is mainly about the ideas, processes, problems, and solutions encountered by our group's final project. In addition, we learned how to take part in the joint work and Python programming experience and the algorithms in pygame through this study and group cooperation.

Stock price movements are linked to lots of factors, whether it is a statement by a celebrity or an event of the magnitude of Covid-19. This paper will mainly focus on CNN (Convolutional neural network) and RNN (recurrent neural network), which are two ways to do research on stock prediction. It will discuss the challenges and limitations associated with using neural networks for stock prediction, including data preprocessing, model training, and generalization to different market conditions. The results of this study will provide insights into the potential of CNNs and RNNs for stock prediction.