DDoS attack analysis in network traffic is important, particularly in decentralized, privacy-conscious domains such as the cloud. Conventional centralized-based detection schemes are privacy-invasive, scale poorly, and do not cope well with non-IID (heterogeneous) data. Federated learning is a promising solution by allowing collaborative model training from distributed clients, approximating a global model from local updates. With a Kaggle DDoS dataset, the work proposed an innovative FedProx-BiLSTM-Attention model with multithreading for fast local training. It delivered 99.84% of accuracy, beating the FedProx-LSTM baseline (98.40%). Attention BiLSTM effectively identifies temporal patterns in network traffic, while FedProx overcomes data heterogeneity, and multithreading boosts training efficiency. The proposed approach excels by integrating these pieces into a strong and scalable federated intrusion detection system. It also showed reduced CPU utilization compared to baseline models, rendering it more resource-conserving in real-world deployment. Measures of evaluation including accuracy, precision, recall, and F1-score attested to the model’s superiority.

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

The design of human-machine interfaces is more precise and demanding in the medical and healthcare industries. Medical monitoring equipment demands more consistent and effective interpretation, as well as fast and straightforward operation due to its monitoring and reference functions. Consequently, it is crucial to consider how people interact with computers when designing the interface for medical monitoring devices. Nowadays people are giving more importance to health than anything in the world. Therefore, as it is related to peoples’ safety, the architecture of human-computer communication must be carefully considered in the studies and development of high-end medical equipment. The price of training physicians and other medical professionals is rising dramatically. Most of the countries have stepped forward from the traditional medical teaching system to a more human computer interactive teaching and learning environment with innovative technologies. This article focuses on the related researches, existing HCI applications for healthcare and the application of deep neural network for disease classification. The proposed work is to develop a healthcare learning platform to offer healthcare education to both medical practitioners and also for common people. This can be implemented as Mobile apps using human-computer interface technology and also as a website with Artificial Intelligence and Machine Learning Techniques. This proposal’s primary goal is to provide anytime, everywhere access to healthcare education for physiological and medical teaching courses, consequently advancing national health care.

With the increased computational efficiency, Deep Neural Network gained more importance in the area of medical diagnosis. Nowadays many researchers have noticed the security concerns of various deep neural network models used for the clinical applications. However an efficient model misbehaves frequently when it confronted with intentionally modified data samples, called adversarial examples. These adversarial examples generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Thus, various adversarial attacks and defense methods certainly stand out from both AI and security networks and have turned into a hot exploration point lately. Adversarial attacks can be expected in various applications of deep learning model especially in healthcare area for disease prediction or classification. It should be properly handled with effective defensive mechanisms or else it may be a great threat to human life. This literature work will help to notice various adversarial attacks and defensive mechanisms. In the field of clinical analysis, this paper gives a detailed research on adversarial approaches on deep neural networks. This paper starts with the speculative establishments, various techniques, and utilization of adversarial attacking strategies. The contributions by the various researchers for the defensive mechanisms against adversarial attacks were also discussed. A few open issues and difficulties are accordingly discussed about, which might incite further exploration endeavors.

Water is the vital component in a human body. Healthy body should be hydrated enough for the proper functioning of various organs. Water has major roles in the physical functioning of a human. Water plays a major role in the metabolic activities in the body. The various nutrients formed in the human body are transferred to different organs through water. Intake of water and water discharge should be controlled to maintain the water balance. Requirements of water in the human body may not meet through the food items or beverages and also not possible to get from metabolic activities. Sometimes the present dehydration level may be life threatening. There should be a proper mechanism to calculate the severity level of dehydration. If the severity of dehydration could be calculated, it is possible to take proper remedies. Dehydration may lead to different chronic diseases like kidney failure, coma, heart related illness, electrolyte abnormalities etc. The intake of plain water is required to maintain the water balance in the human body for better health. It is inevitable to meet the daily water requirements as the deficiency of water in human being may lead to various chronic diseases. Deep learning methods can be used to develop a predictive model for the early diagnosis of chronic diseases with a proper dataset which indudes not only the test results but also the hydration level in human body.

Multi-label disease classification algorithms help to predict various chronic diseases at an early stage. Diverse deep neural networks are applied for multi-label classification problems to foresee multiple mutually non-exclusive classes or diseases. We propose a federated approach for detecting the chest diseases using DenseNets for better accuracy in prediction of various diseases. Images of chest X-ray from the Kaggle repository is used as the dataset in the proposed model. This new model is tested with both sample and full dataset of chest X-ray, and it outperforms existing models in terms of various evaluation metrics. We adopted transfer learning approach along with the pre-trained network from scratch to improve performance. For this, we have integrated DenseNet121 to our framework. DenseNets have a few focal points as they help to overcome vanishing gradient issues, boost up the feature propagation and reuse and also to reduce the number of parameters. Furthermore, gradCAMS are used as visualization methods to visualize the affected parts on chest X-ray. Henceforth, the proposed architecture will help the prediction of various diseases from a single chest X-ray and furthermore direct the doctors and specialists for taking timely decisions.