http://localhost:8081/jspui/handle/123456789/15072 2026-04-20T05:36:04Z 2026-04-20T05:36:04Z Naik, Swapnil Pramod http://localhost:8081/jspui/handle/123456789/20350 2026-04-09T08:00:56Z 2022-05-01T00:00:00Z

Title: NAMED ENTITY RECOGNITION IN BIOMEDICAL DOMAIN Authors: Naik, Swapnil Pramod Abstract: The key task of the Named Entity Recognition (NER) in the biomedical domain is to identify named entities like genes, proteins, diseases, chemicals etc. Compared to normal NER, Biomedical Named Entity Recognition (BIONER) has some additional complexities. As the field is rapidly developing and new information in the field of medicines and biology continues to evolve there has been more development in a lot of new words which need to be constantly added to identity named entities and types. Also, these names are typically longer and combination of many individual terms, many abbreviations are used, and also combinations of letters, symbols and punctuation e.g., BRCA1.

2022-05-01T00:00:00Z Mohammad, Taj http://localhost:8081/jspui/handle/123456789/20349 2026-04-09T08:00:34Z 2022-05-01T00:00:00Z

Title: REAL-TIME VEHICLE IDENTIFICATION, TRACKING AND COUNTING SYSTEM USING YOLO AND DEEPSORT Authors: Mohammad, Taj Abstract: The count of motor vehicles on the road are continuously on the rise since industrial revolution took place. Due to this, quick and reliable vehicle detection, tracking and vehicle counting, on the road are required. However, the present approach focuses on counting the total number of vehicles without considering the direction and heterogeneity of vehicles. Therefore, there is a pressing need to design an efficient method to detect vehicle’s type, track, and count in up and down direction. Considering this in view, we design an efficient method to conquer the aforementioned task by utilizing deep learning methods, You Only Look Once (YOLO) and DeepSORT that helps to identify vehicle’s type, tracking the vehicle and estimate traffic density using YOLOv4 and DeepSORT. Accuracy metric is employed to find the effectiveness of the proposed model . Moreover, the accuracy metric of the presented framework is compared with the latest techniques which demonstrates that the presented framework performs better than the latest approaches: YOLOv3-tiny, YOLOv3, YOLOv4-tiny, DeepSORT by improving the detection accuracy by 12.16 %, 3.07%, and 7.69%, respectively and counting accuracy by 27.50%, 4.40%, and 11.00%, respectively.

2022-05-01T00:00:00Z Jawkhede, Tanmay http://localhost:8081/jspui/handle/123456789/20348 2026-04-09T08:00:21Z 2022-05-01T00:00:00Z

Title: SMISHFILTER : AN ENSEMBLE MACHINE LEARNING BASED MODEL TO DETECT SMS PHISHING ATTACKS Authors: Jawkhede, Tanmay Abstract: In the current technological era, people are attracted to electronic gadgets like laptops and mobile phones, mainly smart phones and tablets, which have become their primary source of entertainment in the current virtual world. People are more attached to their mobile phones due to the easy availability of mobile internet and pocket-friendliness, which exposes various security threats like phishing, Smishing, and information leaks due to malicious app installation. There are multiple mediums through which phishing attacks are generally carried out, like fake websites, Emails, or SMS. Generally, people prefer SMS over mail because it is simple at the same time and doesn’t need an internet connection. We can’t ignore the fact that the cost of SMS has also decreased a lot; hence the use of SMS service has increased. The rise in the usage of SMS services made attackers use it as the medium. Here we have proposed an efficient approach for detecting SMS-based phishing called Smishing. To differentiate between Smishing and legitimate messages, 75 different features are used initially. The results of our experiments show that our approach gives an accuracy of 98.93 % using Ensemble machine Learning Techniques. We integrate an android malware detector, which provides security against information leaks due to APK installation with or without user consent which spread through SMS. Also, we are able to select optimal feature set using various correlation algorithms, which select 18 features and give an accuracy of 98.81%, which is better than earlier work that used a correlation algorithm.

2022-05-01T00:00:00Z Tiwari, Vatsal http://localhost:8081/jspui/handle/123456789/20347 2026-04-09T08:00:01Z 2022-05-01T00:00:00Z

Title: TOWARDS DESIGNING A MODELLING LANGUAGE FOR QUANTUM SOFTWARE AND DEVELOPMENT OF APIS Authors: Tiwari, Vatsal Abstract: Quantum Computing is a revolutionary emerging area of Computer Science, which is in its early phase of development [4]. The concepts of Qubits, superposition, and entanglement allow the ability to manage all the states of a quantum computer at the same time. This gives the quantum computers an exponential performance speedup over the classical counterparts. Quantum computers are predicted to have huge interdisciplinary applications such as security and cryptography, medicines, chemistry, physics, agriculture, and many others [2] [3]. The speed and efficiency provided by quantum computers for these applications would have a big real-world impact. However, a revolutionary change in Quantum Computing has the development of quantum software at its core [4]. As a part of our dissertation, we aim to build a Quantum Software modelling language which will be based on and act as an extension to the work of Perez, Gonzalez and Hector in [8]. The additional part of our work include developing an API containing implementations of Quantum Algorithms segregated based on their domains of work. Due to the counter-intuitive nature of Quantum programming algorithms, choosing the correct algorithm at a given situation may require a comprehensive knowledge of the functioning and technology. Implementing the chosen algorithm into an executable program requires an understanding of the parameters and resources to be used as well as the environment [9]. This is where our work comes in. By developing a collection of quantum algorithms designated to their specific domains of implementation, we attempt to bridge the gap between quantum programming and general software development and programming user-groups by providing abstraction and increasing accessibility of quantum algorithms.

2022-05-01T00:00:00Z