Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction.

Federated learning is an innovative approach that allows collaboration across distributed clients while maintaining data privacy. Despite its numerous benefits, several issues persist in this domain. This comprehensive review examines the critical problems of communication and computation (C&C) costs in cross-silo federated learning environments, which significantly impact the scalability and practical adoption of the system. The research presents a novel multi-dimensional analysis methodology to evaluate cost reduction techniques that consider privacy, accuracy, scalability, and adaptability. The methodical investigation reveals that while existing approaches can substantially reduce communication overhead in controlled environments, ensuring model convergence and privacy guarantees remains challenging across diverse scenarios. Through detailed case studies spanning smart city deployments, healthcare, and finance sectors, the study demonstrates how various C&C optimization strategies perform differently in real-world applications. The review introduces a systematic taxonomy of cost-reduction techniques and proves that hybrid approaches combining multiple optimization methods can maintain model performance while optimizing resource utilization. The review concludes by presenting a unified roadmap for developing adaptive solutions that balance privacy, efficiency, and scalability requirements in cross-silo contexts and identifying crucial research gaps. This compilation of recent developments focuses on areas that require further investigation to enhance real-world deployments and provides practitioners with actionable guidelines for implementing effective federated learning systems.

Federated learning provides a mechanism for different silos to collaborate, and each silo gets aid without compromising privacy. This simulation study is based on healthcare datasets, so the silos are hospitals or healthcare organizations. The selection of hospitals for federated learning increases the overall performance of the model. Cross-silo comes with many challenges, even though the number of participating clients is limited compared to cross-device federated learning. This study considers two of those, which are heterogeneity of data and local performance. An approach called FedDSL based on ’Datasize’, ’Skewness’, and ’Local Performance’ is introduced in this paper. Initially, synthetic data are generated considering the size of the data and skewness, which creates statistical heterogeneity in the cross-silo environment. Once this environment is created, a client selection strategy is applied that uses a weighted approach to select clients. A statistical analysis checks the data distributed among hospitals using skewness and normality tests. Experiments are conducted using the Flower Framework, and FedDSL is compared with random client selection. The model is applied with various aggregation algorithms, including FedAvg, FedProx, and FedAdam. The results show an increased model performance with the FedDSL approach compared to random client selection.

In recent years, the proliferation of the Internet of Things (IoT) has kick started the home and office automation in a very rapid manner. The paper demonstrates a cost effective implementation of an IoT system for managing the visitors in an office environment. The automation system comprises low cost NodeMCU based Wireless Transmitter, NodeMCU based Wireless Display Unit and an android mobile phone. The mobile phone also serves as wireless Access Point to which the Wireless Transmitter and the Wireless Display Units are wirelessly connected for exchanging the messages using UDP protocol. The Wireless Transmitter and the Wireless Display Units are kept in the visitor’s waiting area. The consulting person possesses an android mobile phone in which the automation software is installed. The visitor enters a message using the Wireless Transmitter and notes down the acknowledgement token number sent by the automation software. When the consulting person checks this message, the same token number is sent to the Wireless Display Unit signaling the visitor to consult the person.