Chronic kidney disease (CKD) is a major health issue in the world, and there is a need to apply quick and accurate diagnosis methods to reduce chronic disease. The processing of any medical information is limited in the centralized location due to the significant latency, privacy concerns, and signal variability produced by wearable Internet-of-Things (IoT) devices. In a desire to overcome these shortcomings, the current study suggests a federated learning (FL)-based IoT to predict CKD in real-time preserving privacy. Nutcracker Optimization (ENO) heuristic was further used to select salient features out of CKD datasets, which were pre-trained convolutional architectures, such as ResNet, EfficientNet, AlexNet, and Inception, to extract high-dimensional, discriminative features. The final predictive step was a multi-head deep neural network (M-DNN), and the model was trained locally on distributed devices by use of FL, thus avoiding the exodus of sensitive patient data offsite. The evaluation of the framework was done on both independently and identically distributed (IID) and non-IID datasets, and the framework performed better in each instance. It is worth mentioning that ResNet + ENO + FL + M-DNN pipeline has achieved accuracy of 99.997
In recent years, Unmanned Aerial Vehicles (UAVs) or drones have gained rapid response in terms of security, search and rescue (SAR), border surveillance, etc. Existing monitoring frameworks often struggle to maintain detection consistency when targets undergo significant scale variations due to altitude changes, leading to critical information gaps. To address this issue, this work proposes an integrated real-time detection pipeline for detecting targets through the wireless live drone video feed. Build upon YOLOv8-nano architecture, extensive flight experiments were conducted to determine the detection performance across multiple flight altitudes. Trained on VisDrone2019 dataset, the results of YOLOv8-nano model achieves 57.4
We can see a lot of benefits in various sectors when we incorporate IoT in a system. Botnet assaults is one of the problems in a security system. With the help of these botnet assaults, the IoT networks are vulnerable to attacks. With these, attackers may conduct illegal activities in the network. Since, IoT systems have limited resource, standard security systems are incapable to solve such ever evolving problems. By using AI and ML methods we can enhance the detection of botnet assaults. This paper does a systematic study on various methods adopted for identifying botnet assaults thorough AI and ML.
Generative AI models are transforming biomedical research by enabling thorough analysis of medical literature to evaluate the relevance of medications. This study compares three sophisticated models: BioGPT, GPT-4, and PubMed BERT, in terms of their understanding of medication names and their recognition of applications. Each model is evaluated based on accuracy, contextual relevance, and flexibility using a curated dataset that includes annotated medication names and their corresponding uses. BioGPT, designed specifically for biomedical tasks, excels with specialized terminology but struggles in ambiguous contexts. GPT-4, as a general-purpose model, demonstrates remarkable adaptability and language understanding but lacks deep specialization in biomedical domains. PubMed BERT, tailored to PubMed literature, achieves a compromise between accuracy in domain-specific contexts and contextual awareness, benefiting from extensive training on biomedical texts. This analysis highlights the trade-offs between general versatility and specialized expertise, providing insights into choosing suitable models for various healthcare and pharmaceutical applications. These findings guide the integration of generative AI into clinical decision-making and medical research activities.
The goal of city planning and design is to create resourceful, sustainable, and efficient cities by managing resources, infrastructure, and areas that improve quality of life. By strengthening information and communication technology and enhancing decision-making abilities based on data-driven insights, the incorporation of AI into urban planning makes cities more competitive. Better development is accelerated by artificial intelligence (AI) techniques, including Building Information Modeling (BIM), Big Data Analytics, AI models, Traffic and Mobility Management, Energy and Environmental Impact Analysis, and Disaster Resilience Planning. AI-powered urban planning has been a subtle topic with many facets, including privacy and ethical issues, low awareness, and accessibility challenges. It deserves more attention, though, because it has potential to make smarter, more sustainable cities with the right research and application.
