Objective: The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an exponential scale. It has been observed that concentration on datasets drawn from supporting primary sources using machine learning algorithms brings the accuracy expected for cancer diagnosis. Methods: This research presents an innovative cancer classification technique that combines fast minimum redundancy-maximum relevance-based feature selection with Binary Portia Spider Optimization Algorithm to optimize features. The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. Results: The classification efficiency of the advanced model is tested on six different cancer datasets that exhibit classification challenges. The empirical analysis confirms that the proposed methodology FmRMR-BPSOA is effective since it reached the highest accuracy of 99.79%. The result is of utmost significance as the proposed model emphasizes the need for alternative and highly efficient greater precision cancer diagnosis. The classification accuracy concludes that the model holds great promise for real-life medical implementations.

Cancer is a disease that results from the unnatural proliferation of aberrant cells that infest the body’s healthy cells and spread throughout the body. Lung cancer is characterized by an imbalance in the cells of the affected organs, namely the lungs. The prediction of lung cancer at an early stage is very important, particularly in countries that are densely populated and have lower incomes. Clinically conventional approaches, such as blood tests and other types of treatments, are used by specialists. The age of artificial intelligence (AI) has begun, and today, it is feasible to construct a computer-aided diagnostic mechanism with the assistance of machine learning and deep learning algorithms. In this particular piece of research, one deep learning algorithm, an artificial neural network (ANN), has been investigated to determine whether or not lung cancer could be detected at an earlier stage. In addition to conventional ANN, ensemble ANN with weighted averaging and soft and hard voting ensemble techniques are also considered. In order to achieve this effectiveness, the state-of-the-art parameters for the proposed method using ANN are assessed and evaluated using the lung cancer dataset. The empirical analysis shows that hard voting-enabled ANN shows the highest accuracy at 97.47%.

This work proposes an intelligent cybersecurity system built upon Artificial Intelligence (AI) to address evolving cyber threats in heterogeneous Internet of Things (IoT) environments. The proposed framework integrates machine learning with mathematical threat analysis to shift from traditional system security, which responds after an attack, to a proactive approach that predicts and prevents threats. It reacts immediately, processes in just 0.35 s, adapts to 95% of IoT surroundings, and handles security by categorizing threats into four tiers with minimal impact on performance. Tests against standard Intrusion Detection Systems (IDSs), such as SNORT, Suricata, and Bro/Zeek, demonstrate that the framework is superior at handling a wide range of threats.

We focus on developing an Automatic Plant Watering System using Arduino and ESP32 to enhance plant care through automated irrigation and predictive health analysis. The system integrates a soil moisture sensor to monitor moisture levels continuously and activate a water pump when the soil moisture drops below a specified threshold, ensuring optimal watering. In addition to automated irrigation, the system assesses plant health by analyzing environmental parameters such as temperature, humidity, and soil moisture levels. These data are collected in real-time, stored in the cloud, and processed to predict the plant's health status. The real-time data and health predictions are displayed on the Blynk app or website, providing users with a convenient way to monitor their plants remotely. By leveraging IoT technologies, cloud storage, and predictive analytics, this system aims to minimize human intervention, improve plant health management, and offer a sustainable solution for plant care.

Grain quality plays a crucial role in agriculture, affecting health, economic efficiency, and market pricing. Traditional manual testing methods are often slow, inconsistent, and labour-intensive. This project presents an automated system that leverages image processing and machine learning to transform grain quality assessment and price estimation, offering direct benefits to farmers and stakeholders throughout the supply chain. High-resolution images of grains, captured under controlled lighting conditions, are processed to extract essential features such as size, color, shape, and texture. These are the key features for identifying grain types and evaluating quality metrics like moisture content, protein levels, and impurities. The system utilizes a Multilayer Perceptron (MLP) as its final model for classification and analysis. Trained on extensive datasets of labelled images, the MLP ensures precise grain classification, impurity detection, and quality evaluation. Furthermore, it estimates grain prices based on these quality metrics, enabling farmers to receive fair compensation for their produce and make informed decisions regarding market timing and quality enhancement. By automating grain testing and pricing, this non-destructive approach eliminates human error, preserves grain integrity, and offers a reliable, efficient, and scalable solution. Farmers benefit from quicker quality assessments, enhanced pricing transparency, and reduced reliance on manual testing, empowering them to optimize productivity and profitability in a competitive market.