Leukemia is a very heterogeneous and complex blood cancer, which poses a significant challenge in its proper categorization and diagnosis. This paper aims to introduce various deep learning architectures, namely EfficientNet, LeNet, AlexNet, ResNet, VGG, and custom CNNs, for improved classification of leukemia subtypes. These models provide much improvement in feature extraction and learning, which further helps in the performance and reliability of classification. A web-based interface has also been provided through which a user can upload images and clinical data for analysis. The interface displays model predictions, symptom analysis, and accuracy metrics. Data collection, preprocessing, normalization, and scaling are part of the framework, considering leukemia cell images, genomic features, and clinical records. Using the preprocessed data, training is performed on the various models with thorough testing and validation to fine-tune the best-performing architecture. Among these, AlexNet gave a classification accuracy of 88.975%. These results strongly underscore the potential of advanced deep learning techniques to radically transform leukemia diagnosis and classification for precision medicine.

Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location.

In this groundbreaking study, we propose an innovative approach to tackle the formidable task of early detectionand accurate prediction of kidney diseases. By harnessing the potential of a comprehensive healthcare dataset andleveraging a machine learning model originally developed for kidney disease diagnosis, our methodology integratesintelligent feature selection techniques. These techniques, including heuristic based feature selection andevolutionary gravitational search-based feature selection (EGS-FS), allow us to identify the most informative featuresfor accurate prediction. Classification is performed using our newly designed Intelligent Deep Learning basedClassifier, which is further optimized using the cutting-edge Artificial Gorilla Troops Optimizer algorithm. Toassess the performance of our proposed model, we conduct a thorough evaluation and comparison against existingmethods using a range of statistical measures. Remarkably, our experimental results on the widely recognizedChronic Kidney Disease dataset showcase an exceptional accuracy value of 99%. This research not only contributesto the advancement of kidney disease prediction but also provides invaluable insights for efficient patientmanagement. By embracing this novel approach, clinicians can make informed decisions and revolutionize the fieldof kidney disease detection and treatment.