This research project aimed to develop a highly efficient and effective Chrome extension 
that could classify tweets containing hate speech, along with conducting sentiment and 
topic analysis. Hate speech is a persistent and concerning issue on Twitter, yet the platform 
has made little effort to address it. To address this challenge, this research performed a 
series of experiments, including the use of Support Vector Machine (SVM), Random 
Forest, and Long Short-Term Memory-based (LSTM) neural network classifiers. The 
results of the experiments showed that the SVM classifier, combined with word2vec [1] 
feature engineering, outperformed all other methods. 
Then developed the Chrome extension using a monolithic repository architecture, utilizing 
React and Django. By implementing this extension, users are able to automatically analyze 
live tweets and identify hate speech content, along with obtaining sentiment and topic 
analysis. The outcome of this research project could provide a significant contribution 
towards a more positive online environment and towards curbing the prevalence of hate 
speech on Twitter.

Can deep learning approaches achieve similar accuracy as statistical models in predicting 
the stock market? This research introduces an approach integrating Generative Adversarial 
Networks (GANs) with Long Short-Term Memory (LSTM) generators and Convolutional 
Neural Network (CNN) discriminators to predict stock trading actions. By analyzing and 
computing on historical Open, High, Low, Close and Volume (OHLCV) data of the stock 
market, we generate a wide range of input features using Fourier transforms, auto-encoders, 
and several technical indicators. Extreme Gradient Boosting (XGBoost) is employed to 
filter and extract important features. The extracted features are fed into the LSTM 
generator, whose output is then fed to the CNN discriminator to discriminate the buy, sell, 
or hold signals. Results demonstrate the model's effectiveness in capturing temporal and 
spatial patterns, with implications for refining stock trading strategies and enhancing 
financial decision-making for maximizing profits.

Hordeum vulgare, known as Barley in English and Jamara in Nepali, is used in the Dashain festival traditionally. For this project, the seeds of Barley were collected from the Kathmandu district of Nepal and observed in three different samples. Seeds sowed in this study were kept under different light conditions. The samples were kept under regular sunlight, LED light source with a color temperature of 6500K and in a dark room with no light source separately for a period of 15 days. If the plants were exposed to various kinds of light it was observed that growth of the plants is influenced by types of light. It was also found that each of the above-mentioned sources of light affected the plant growth differently. Out of the three samples that were taken; the first sample was exposed to direct sunlight while the second sample was exposed to light with a daylight color temperature of 6500 Kelvin and the third sample was placed in a room with no exposure to light. This experiment drew conclusions based on the differences in growth rate, the color of the leaves, the number of branches developed from a seed, the formation of roots, and the strength of anchorage.

Neural networks, inspired by the biological structure of the brain, have become a cornerstone in various machine learning tasks, including but not limited to those within the fashion domain. Convolutional Neural Networks (CNNs) are specialized neural networks designed for processing structured grid data - particularly images. Using these neural networks, The Virtual Wardrobe Fashion Advisor was created. Additionally programming languages like Python for algorithm implementation and Django,Html, Javascript and Cascading Style Sheets for web based application development were also used. The model was approached in the traditional CNN method to detect the type of clothes users prefer. Model detects the clothes with the help of selective gender and seasons; a setting in accordance to the user preference.

This project introduces SocketDB, a novel real-time SQL database system specifically engineered to meet the evolving demands of contemporary applications requiring instantaneous data updates and robust query processing capabilities. By integrating a WebSocket-based notification system, SocketDB offers a unique approach, allowing clients to subscribe and instantly receive updates, thus facilitating real-time interactions and analytics. Built on a columnar data layout, it ensures efficient query performance and storage optimization, particularly beneficial for read-intensive scenarios. The core of SocketDB is enhanced by a recursive descent SQL parser, enabling comprehensive support for a wide array of SQL operations, making it highly adaptable to complex data manipulation needs. Additionally, SocketDB incorporates advanced features such as data compression with the Zstandard algorithm, ensuring reduced storage footprint without compromising access speed. Embedded fault tolerance mechanisms and scalable infrastructure further reinforce its reliability and applicability in diverse environments. Aimed at developers and businesses alike, SocketDB is poised to transform real-time data handling, offering a scalable, efficient, and user-friendly platform for dynamic data management and analysis.

FPGAs are an arrangement of gates that can be reprogrammed or reconfigured. In FPGA, Logic functions are usually implemented by Logic Blocks, Programmable Routing, and Input-output Blocks. FPGAs are more adaptable than ASICs but they are relatively larger in size, slower and consume more power because of routing which uses almost 90% of the overall area in FPGA. Our work explains detailed analysis of the existing techniques addressing different issues such as; routing, mapping, cluster, optimization, and energy efficiency related to FPGA power, speed delay and area density. By analyzing different FPGAs architecture techniques, we revealed that low power consumption and fast speed can be achieved by designing FPGA with large cluster size, however efficient area can be achieved by designing FPGA with small cluster size. Lookup table with four input gives most effective tradeoff between Power, speed and Area. This paper also suggests the latest area for optimizing the power, speed delay and area density for different FPGA architecture.

The pedagogical approaches in this 21stcentury world have been modified in many ways with the rapid advancements and innovations in science and technology. The modes of teaching and learning such as distance learning (DL), online learning (OL), and blended learning (BL) are some of the modalities that are becoming popular at present. For some years now, blended modality (BM) of learning is spreading out rapidly and widely as a new pedagogical approach. In recent years, the use of BM is gradually increasing in Nepalese educational institutions too. This study has made an attempt to explore the students’ perception towards blended modality of learning; and its benefits and challenges as a learning approach in the context of higher education in Nepal. Based on a survey- based questionnaire and semi-structured interview as research tools, it has been found in the study that the students had positive attitudes towards BM, and it was very useful to those learners in higher education, who needed to take family and job responsibilities together with their study. Besides, it was reported that though Internet connectivity was one of the main problems, the students were able to take the advantages of both F2F (face to face) and OL through BM.

Character recognition is commonly referred to as Optical Character recognition as it deals with the recognition of optically processed characters. With the advent of digital optical scanners, a lot of paper- based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. OCR is an instance of off-line character recognition, where the system recognizes the fixed static shape of the character. This paper focuses on character recognition of printed text in Nepali script. This work analyzes the efficiency of Nepalese OCR based on Tesseract engine. The benchmark of this investigation and analysis is to create the dataset of the 69 different fonts with the 2,484 samples of consonants data of Nepali script. The overall accuracy of 96% was obtained in the training phase and 69% in the testing phase.

According to the World Health Organization (WHO), around 1 billion of the world’s population, i.e., around 15% of all people, live with some form of disability. In Nepal, around 2% of the total population has some kind of disability. Most of the disabled population is of a low socioeconomic status. However, the generic studies and surveys that exist are largely varied and are likely to be inaccurate. Person with Disabilities (PwD) face utter challenges and difficulties in areas from their normal livelihood to employment opportunities. Various organizations and activists have been working toward raising the socioeconomic status and quality of life of PwD. With the advent of new technologies, especially in Information and Communications Technology (ICT), that promote accessibility via different assistive devices, the possibility of inclusion of PwD in education and employment has become apparent and many organizations throughout the world have become inclusive. The potential of ICT to transform the lives of PwD should be addressed by the government and other organizations working for the benefit of PwD by formulating and implementing the policies. This paper analyzes various aspects on Disability, Employment, the Role of ICT, and Accessibility to Empower the Life of PwD in Nepal.

Nepal’s public education has a somewhat dubious history. Over the course of more than a century it has seen a rapid growth playing an instrumental role bringing about changes in the sociopolitical arena. However, its qualitative aspect has always been questioned while its quantitative aspect has been somewhat appreciated. In order to enhance the quality of education, especially in the last two decades, ICT, Information and Communication Technology, has been looked into as one of the potential elements in bringing about much needed qualitative improvement in Nepal’s education system. As a result, attempts have been made to make use of ICT in its public education system from both government and non-governmental agencies. This paper tries to understand historical development of ICT, public education system and attempts being made to integrate ICT in public education.