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Öğe A Comprehensive Survey of Predicting stock market prices: an analysis of traditional statistical models and machine-learning techniques(American Institute of Physics, 2024) Patel, Divy; Patel, Warish; Koyuncu, HakanThe stock market has witnessed a remarkable surge in popularity in recent years, attracting investors from all walks of life. However, predicting stock values remains a daunting task due to financial markets' inherent unpredictability and complexity. Despite these challenges, the stock market offers a dynamic and ever-changing platform for traders to invest in shares, with the potential for significant gains and losses. For investors, accurate forecasting of stock prices is crucial as it provides invaluable insights into a company's financial health and growth prospects. With this information, investors can make informed decisions, mitigate risks, and capitalize on lucrative opportunities in the market. As a result, extensive research has been dedicated to developing effective prediction methods, leveraging various mathematical models and machine-learning techniques. This research paper delves into the realm of stock market prediction, explicitly focusing on evaluating different machine-learning styles. The primary objective is to comprehensively analyze and compare the performance of these techniques in forecasting stock market behavior. By understanding the strengths and limitations of each method, investors, financial analysts, and market participants can gain critical knowledge to optimize their trading strategies and decision-making processes. To achieve this goal, the study explores an array of machine-learning algorithms, ranging from traditional linear regression models to sophisticated deep-learning approaches. These algorithms leverage historical stock market data, macroeconomic indicators, company financials, and sentiment analysis, among other factors, to predict future price movements and market trends. In addition to performance comparison, the research paper examines the impact of various factors that influence the effectiveness of these machine-learning techniques. Factors such as data quality, feature engineering, model selection, hyperparameter tuning, and market conditions play pivotal roles in the accuracy of predictions. Understanding these factors will aid in refining the model-building process and enhancing overall forecasting capabilities. The study encompasses an extensive dataset spanning multiple stock markets and periods, ensuring robustness and reliability in the findings. Performance evaluation metrics, including mean squared error, accuracy, precision, recall, and F1 score, will be employed to assess the predictive power of the machine-learning techniques objectively. Furthermore, the paper investigates the potential of ensemble methods, combining the strengths of multiple models to achieve enhanced prediction accuracy. Ensemble techniques, such as bagging, boosting, and stacking, have proven effective in diverse domains and are expected to demonstrate their value in stock market prediction. By the end of this research, readers will have a comprehensive understanding of the landscape of machine-learning techniques applied to stock market prediction. The findings will offer insights into which methods are most suitable for different market conditions and will aid in establishing best practices for effective and reliable stock market forecasting. In conclusion, this research paper serves as a valuable resource for investors, financial analysts, and researchers, thoroughly assessing machine-learning techniques' efficacy in predicting stock market behavior. It contributes to the growing body of knowledge in financial technology. It underscores the critical role of data-driven decision-making in navigating the complexities of the modern stock market.Öğe A hybrid ensemble learning approach for efficient diabetic retinopathy prediction and classification using machine learning and deep learning techniques(Ismail Saritas, 2024) Shah, Arpit; Patel, Warish; Koyuncu, HakanAI is a crucial tool in early detection and classification of diabetic retinopathy, which is a leading cause of visual impairment globally. Transfer Learning (TL) was used to improve the accuracy of predictions and classifications within training datasets, surpassing existing methodologies. The study provides comprehensive insights into current databases, screening programs, performance evaluation metrics, relevant biomarkers, and challenges encountered in ophthalmology. The findings underscore the potential of AI-based approaches in enhancing diagnostic precision and offer a promising direction for future studies. The paper concludes by delineating opportunities for further research and development in integrating AI advancements in the field. Conclusion: The findings underscore the efficacy of Transfer Learning in significantly improving the accuracy of diabetic retinopathy image predictions. This research highlights the potential of AI-based approaches in enhancing diagnostic precision and offers a promising direction for future studies. The paper concludes by delineating opportunities for further research and development, emphasizing the continued integration of advanced AI methodologies in ophthalmology to advance diabetic retinopathy detection and management.Öğe Empowering healthcare innovation: IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape(Taru Publications, 2024) Shah, Arpit; Patel, Warish; Koyuncu, HakanBackground: Among prevalent medical complications, Diabetic Eye Disease (DED) stands as a significant contributor to vision loss. To forecast its progression and accurately assess the various stages, diverse methodologies have emerged. Machine Learning (ML) and Deep Learning (DL) algorithms have become essential tools in this endeavor, primarily through their adept analysis of Diabetic Retinopathy (DR) images. However, there is still a need for a more efficient and accurate method to predict DR performance. Method: We have developed an innovative method for classifying and predicting diabetic retinopathy. The novel idea in this research is to combine several techniques, including ensemble learning and a 2D convolutional neural network; we utilized transfer learning and a correlation method in our approach. Initially, the Stochastic Gradient Boosting process was employed for predicting diabetic retinopathy. We then used a boosting-based Ensemble Learning method for predicting images of diabetic retinopathy. Next, we applied a 2D Convolutional Neural Network. We successfully employed Transfer Learning to classify different stages of diabetic retinopathy images accurately. This research explores the role of artificial intelligence in identifying and categorizing diabetic retinopathy at an early stage, using techniques such as machine learning and deep learning. It also use techniques like transfer learning, domain adaptation, multitask learning, and explainable AI to accurately classify different stages of diabetic retinopathy images. Our proposed technique achieves impressive results through experiments, with a 97.9% accuracy in forecasting DR images and a 98.1% accuracy in image grading. Additionally, sensitivity and specificity metrics measure 99.3% and 97.6%, respectively. Comparative analysis with existing methods underscores the high predictive accuracy achieved by our proposed approach.Öğe Revolutionizing Cancer Diagnosis in IoMT with a Novel Lightweight Deep Learning Model for Histopathological Image Classification(Institute of Electrical and Electronics Engineers Inc., 2023) Patel, Warish; Koyuncu, Hakan; Ganatara, AmitOur proposed solution for breast cancer diagnosis offers a highly reliable and efficient alternative to the traditional manual examination method. By utilizing deep learning models designed with Convolutional Neural Networks (CNN), We have created a breast cancer detection system that is automated, inexpensive, and easy to transport. A system that significantly reduces the risk of delayed diagnosis and saves lives. This approach relies on an integrated ultrasensitive micro-bio-heat sensor array structured as a 3x3 grid using Altium software. The array functions as an embedded system aiming for the early identification of breast cancer. Leveraging IoT advancements, this system can connect with a server through smartphones. This study demonstrates the effectiveness of switch getting to know in histopathological photo classification. This research assesses the effectiveness of CNN models, both with and without transfer learning. It uses a pre-Trained VGG16 model for image classification and demonstrates its successful implementation on a Raspberry Pi. This highlights its efficiency when running on a lightweight and portable processor. Our experimental results show that our system achieves an accuracy of 78% on the BreakHis database and can run on a Raspberry Pi device with minimal resources. © 2023 IEEE.Öğe Security threat analysis and countermeasure using ML in cloud(American Institute of Physics, 2024) Kushwaha, Akhilesh; Patel, Warish; Koyuncu, Hakan; Parikh, Swapnil; Chauhan, AnkitThe rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time.
