Detecting diseases in tomato leaves at an early stage is crucial for preventing crop damage and improving food security. Traditional diagnostic methods are often inefficient, requiring significant expertise and time. To address this challenge, we explore AI-driven approaches, integrating DL and ML methods for automated disease detection. This study employs CNNs, specifically leveraging the VGG16 architecture for feature extraction. Additionally, we compare its effectiveness with classical classifiers such as KNN and SVM. Using a publicly available dataset of healthy and diseased tomato leaves, our results indicate that CNN-based models outperform conventional machine learning classifiers in both accuracy and efficiency. Moreover, integrating IoT-based analytics enhances early detection, reducing crop losses and promoting sustainable agricultural practices.

Heart disease is a significant cause of death worldwide, and early prediction is vital for prevention and treatment. This project uses the Framingham Heart Study dataset for the early prediction of Coronary Heart Disease (CHD) using machine learning methods. The Framingham Heart Study is a highly unbalanced dataset, with only 16 % cases of CHD, which impacts the accuracy of the model. To overcome this, data augmentation techniques such as SMOTE and cGAN are applied to create synthetic cases of CHD. The machine learning algorithms that are compared: Random Forest, XGBoost, SVM, and MLP. XGBoost has achieved the highest AUC-ROC of 0.973 when cGAN-augmented data is used, while cGAN-augmented data improves recall and overall model performance significantly. This study identifies the potential for combining machine learning with data augmentation to improve CHD prediction.

The trading world often appears mysterious, filled with stories of fear, hope, addiction, and occasional profits. However, many fail to recognize that consistent profitability in trading is driven by discipline, a well-defined strategy, and strict adherence to rules. This lack of awareness is a key reason why 75-90% of new traders enter the market with high expectations but end up losing their hard-earned money. In this research we propose a quantitative trading strategy based on exponential moving average (EMA) crossovers, volume analysis, and structured profit booking. The strategy utilises a short-term 9-period EMA and along-term 15-period EMA to identify trend reversals, generating buy signals when the two different EMA's crosses under some conditions and sell signals are generated when the opposite occurs. Meanwhile, a confirmation mechanism is introduced, requiring the price to move at least 0.06% above the crossover price while ensuring the crossover candle remains bullish. Additionally, volume conditions are incorporated to validate momentum, ensuring buy signals are triggered only when the trading volume increases in ascending order. To optimize trade management, a multi-tier profit booking system is implemented, allowing partial exits at predefined levels. which ensures that the traders secure gains while allowing profitable trades to run. The strategy's performance is evaluated through historical back-testing, assessing profitability, accuracy, and risk-reward dynamics. The results demonstrate the effectiveness of integrating EMA crossings with volumes and structured exit points in improving trade success rates. This might become the future of so many people to convert their portfolio from a losing streak to a winning streak.

Edge computing has become a game-changing technology in both academics and industry as the need for quicker and more effective data processing increases. Edge computing lowers latency and improves real-time analysis by processing and storing data close to its source. The increasing significance of edge computing in the current Information Technology architecture demands the critical need to secure its distributed nature. The integration of blockchain-based technologies with edge computing will be addressed in this chapter. Blockchain and edge computing integration can improve security. At the network edge, blockchain technology guarantees data integrity, authentication, and confidentiality. Secure access control mechanisms for distributed resources are made possible by this integration. It reduces the possibility of single points of failure or concentrated attacks or malicious attacks. Furthermore, it makes tamper-proof transactions possible by utilizing widespread acceptance technique like smart contracts. This technology is used as a strategic approach to improve edge computing systems' security architecture. Key security concerns including data integrity, privacy, and access management are addressed by blockchain. This is known for its decentralization, cryptographic integrity, and elimination of middlemen. These issues are made worse by the distributed nature of edge computing. This analysis, which is based on a thorough examination of the literature, emphasizes how blockchain technology may be used in edge contexts to enforce data protection, guarantee transparency, and provide trustworthy audit trails.