There are 1641 companies listed on the National Stock Exchange of India. It is undoubtedly infeasible for a retail investor to invest in all the stocks. It is a well-known fact that the portfolio’s return is an average return of all its constituent stocks, and risk will be less than or equal to the maximum risk of all the portfolio components. This paper is unique as it elaborates on the entire portfolio selection, optimization, and management process. Portfolio selection is accomplished through the K-Means algorithm. Optimization is achieved utilizing the genetic algorithm, and a sliding window is applied for portfolio management. Four different ways of portfolio calculation, namely, equally-weighted portfolio, global minimum variance portfolio, market cap-weighted portfolio, and maximum Sharpe ratio portfolio, are applied. The results depict that all three optimized portfolios outperform the Nifty index. The dataset for the study is obtained from globaldatafeeds.in.

Question Answering (QA) systems are increasingly essential in educational institutions, enhancing both learning and administrative processes by providing quick and accurate answers to user queries. However, existing systems often struggle with accurately classifying and responding to diverse and context-dependent questions, especially when dealing with large knowledge graphs. Predicting the domain of a question can significantly narrow down the search space within a vast knowledge graph, improving the system’s efficiency and accuracy. This study addresses this gap by developing and evaluating domain prediction models. We compare the performance of various deep learning architectures, including Bi-GRU, Bi-LSTM, GRU, and LSTM. Our results demonstrate that the 1-layer Bi-GRU model outperforms the others, achieving the highest test accuracy of 82.13%. Additionally, by employing an ensemble technique that combines models with highest performance measures from each architecture, we further enhance overall performance, achieving an accuracy of 87.14%, which demonstrates improved predictive capability. This work is significant as it provides a robust solution for improving the accuracy and relevance of QA systems in educational settings, thereby enhancing user satisfaction and operational efficiency.

Sentiment Analysis (SA) is a well-known and emerging research field in the area of Natural Language Processing (NLP) and text classification. Feature engineering is considered to be one of the major steps in the Machine Learning (ML) pipeline with effective feature extraction playing a vital role in improving the performance of the SA tasks. Choosing an appropriate feature from the text is considered to be the most challenging task in text classification. This study examines the implementation of different traditional feature extraction models such as Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), N-grams, and word embeddings like Word2Vec and Bidirectional Encoder Representations from Transformers (BERT) on an Amazon review dataset. Furthermore, the hyperparameters of the BERT model were fine-tuned, optimizing its performance on the SA task. Additionally, this research explores the effectiveness of combining the word vectors generated using TF-IDF and BERT for SA. The proposed hybrid model implements an effective negation handling approach and combines TF-IDF with BERT to improve performance for Amazon product review classification.The hybrid model was evaluated using several performance metrics, including accuracy, recall, precision, and F1-score. The proposed hybrid model shows promising results achieving an accuracy of 88%. The integration of BERT and TF-IDF not only enhances the model’s ability to understand and interpret text but also demonstrates the potential of combining advanced and traditional NLP techniques.

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

The early detection and treatment of ovarian cancer face considerable hurdles due to its complexity and lethal nature. Because of its high death rates and heterogeneity, ovarian cancer poses a significant challenge to oncology. In-depth study of ovarian tumor datasets is crucial to improve the knowledge on this complicated illness and to develop new diagnostic and treatment approaches. The accuracy of the information utilized for training and analysis has a substantial impact on how well computer models predict and comprehend ovarian cancer. Data mining methods mostly rely on the quality of data. Hence, in order to improve the accuracy and dependability of ensuing studies, this work is carried out to examine the critical preprocessing methods that are used on ovarian tumor dataset. A novel ovarian tumor dataset is collected and this raw dataset has missing values, incomplete data, noisy data, redundant data and outliers and these anomalies degrade the performance of mining results. In this study, we explore the application of data mining preprocessing methods to enhance the analysis of ovarian tumor datasets. Through the use of methods like feature selection, data cleaning, normalization, and dimensionality reduction, we aim to improve the quality of the data, and make it easier to find significant patterns and biomarkers linked to ovarian cancer. The work emphasizes the importance of preprocessing in maximizing the potential of ovarian tumor datasets and expanding the field’s understanding of this debilitating illness in order to improve detection and treatment process. Preprocessing performance indicators namely accuracy, sensitivity, and specificity are used to assess the efficiency. It is found that, after preprocessing of the dataset, an accuracy of 88% is achieved when classified as benign or malignant using Logistic Regression. Upon applying every feature selection technique on the dataset, it is evident that features obtained through Recursive Feature Elimination technique and feature importance yield greater accuracy of 92% when classified with respect to Logistic Regression and Support Vector Machine. It is expected that the knowledge gathered from these preprocessing techniques result in more precise and trustworthy computer models, which could enhance patient outcomes in the field of ovarian cancer.