Khairum Islam

Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Genetic algorithm has been utilized to identify the most significant biomarkers. The XGBoost classifier is then trained on the selected features and high ROC-AUC scores of 0.864 and 0.911 have been obtained for the premenopausal and postmenopausal populations, respectively. Lack of explainability is one major limitation of current AI systems. The stochastic nature of the ML algorithms raises concerns about the reliability of the system as it is difficult to interpret the reasons behind the decisions. To increase the trustworthiness and accountability of the diagnostic system as well as to provide transparency and explanations behind the predictions, explainable AI has been incorporated into the ML framework. SHAP is employed to quantify the contributions of the selected biomarkers and determine the most discriminative features. Merging SHAP with the ML models enables clinicians to investigate individual decisions made by the model and gain insights into the factors leading to that prediction. Thus, a hybrid decision support system has been established that can eliminate the bottlenecks caused by the black-box nature of the ML algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy obtained from the proposed system outperforms the existing methods as well as the state-of-the-art ROMA algorithm by a substantial margin which signifies its potential to be an effective tool in the differential diagnosis of OC.

Global food security is seriously threatened by plant diseases, which annually cause large losses in agricultural productivity. Early diagnosis and accurate classification of plant diseases are required for disease management programs to be implemented promptly and efficiently. In the area of plant disease classification, Convolutional Neural Networks (CNN) have demonstrated encouraging results in recent years. In this study, we propose a CNN based approach for plant disease classification using a MobileNetV2 based model and transfer learning. The proposed model leverages the MobileNetV2 architecture, known for its lightweight and efficient design, making it well-suited for resource-constrained environments. The pre-trained MobileNetV2 model is modified using transfer learning to accommodate the goal of classifying plant diseases. The model benefits from the characteristics that have been learned from a large-scale dataset through the use of pre-trained weights, leading to improved generalization and reduced training time. We use a standard plant disease dataset with a filtering method as a preprocessing strategy in extended trials to assess the efficiency of the proposed approach. The performance of the model is compared using several cutting-edge techniques, including VGG16, AlexNet and InceptionV3. The experimental findings show that the suggested model performs competitively in classifying plant diseases, surpassing other approaches with an accuracy of 98.56%.

In recent years, social media platforms, particularly twitter, have emerged as crucial sources of public opinion and sentiment. Analyzing sentiment on twitter data presents a significant challenge due to the platform's inherent characteristics, such as brevity, informality, and the prevalence of slang and emojis. This research paper proposes a method for twitter sentiment analysis by leveraging the power of a transformer-based model called RoBERTa. The proposed strategy employs RoBERTa due to its exceptional performance in various natural language processing tasks. Our system captures intricate contextual information and semantic nuances in tweets, making it well-suited for sentiment analysis on this challenging platform. To build an effective sentiment analysis system, the architecture is fine-tuned using a large corpus of twitter data, annotated with sentiment labels. Additionally, we explore various strategies to handle the unique characteristics of twitter data, including tokenization, handling hashtags, user mentions, and URLs, as well as the incorporation of emojis and emoticons. We compare the performance of our model with three other standard machine learning and deep learning models, such as Decision Tree (DT), Support Vector Machine (SVM), and Long Short Term Memory (LSTM) in order to show that our model is superior at correctly analyzing twitter sentiment. The model showcases an exceptional accuracy of 96.78%, highlighting its effectiveness in understanding and classifying sentiment within the context of tweets.

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain.