The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as ‘Cascaded Object Detector’ (COD) and the other is a composition of traditional customised methods, individually known as ‘Colour Transformation’: ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola‐Jones) COD generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro‐controller based miniature electronic devices in terms of its run time complexity of O ( n ³ ) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

The study offers a thorough examination of the accumulation and distribution of wealth among billionaires through the application of big data analytics methodologies. This research centres on an extensive dataset known as "Billionaires.csv," which encompasses a range of information about billionaires from diverse nations, including their demographic characteristics, company particulars, sources of wealth, and more details. The study aims to get a deeper understanding of the determinants that change the net worth of billionaires and detect trends in the worldwide financial system that can guide entrepreneurial ventures and investment possibilities. The dataset is subjected to analysis and visualisation through the utilisation of Python tools and libraries, including but not limited to Pandas, NumPy, Matplotlib, and Seaborn. The results of this study offer valuable insights into the distribution of wealth among billionaires, the factors that contribute to industry success, gender disparities, age demographics, and other factors that influence the accumulation of billionaire wealth.

Phishing attacks have continued to increase globally over the past several decades and remain a major entry point for multi-vector cyberattacks. As conventional single-method detection techniques often struggle to identify increasingly sophisticated phishing attempts, researchers have increasingly focused on AI-based hybrid detection approaches. This systematic review, conducted in accordance with the PRISMA 2020 statement, provides a focused synthesis of these approaches. A comprehensive search was conducted across seven academic databases and one scholarly search engine. Three reviewers independently screened the records, extracted the data, and conducted the methodological quality assessment, with disagreements resolved through discussion and consensus. Following a multi-stage screening process, 18 peer-reviewed studies published between January 2020 and June 2026 were included. The methodological quality of each study was evaluated using a structured quality-assessment checklist. The included studies were synthesized narratively with emphasis on hybridization techniques, data modalities, model architectures, evaluation metrics, strengths, limitations, and future research directions. The findings indicate that AI-based hybrid approaches generally achieved strong benchmark performance and improved robustness against the phishing scenarios evaluated within the included studies. However, limitations were identified in relation to explainability, generalizability, computational complexity, and real-time deployment. The review also identified continuing challenges involving the trade-off between accuracy and efficiency, dependence on unimodal or benchmark datasets, limited reproducibility reporting, and insufficient real-world validation. Future research should prioritize lightweight and fairness-aware hybrid architectures, multimodal data integration, continual learning, and stronger model interpretability to support reliable and scalable deployment in real-world environments.