Background: The modern society is extremely prone to many life-threatening diseases, which can be easily controlled as well as cured if diagnosed at an early stage. The development and implementation of a disease diagnostic system have gained huge popularity over the years. In the current scenario, there are certain factors such as environment, sedentary lifestyle, genetic (hereditary) are the major factors behind the life threatening diseases such as ‘diabetes.’ Moreover, diabetes has achieved the status of the modern man’s leading chronic disease. So one of the prime needs of this generation is to develop a state-of-the-art expert system which can predict diabetes at a very early stage with a minimum of complexity and in an expedited manner. The primary objective of this work is to develop an indigenous and efficient diagnostic technique for detection of diabetes.Method & Discussion: The proposed methodology comprises of two phases: In the first phase The Pima Indian Diabetes Dataset (PIDD) has been collected from the UCI machine learning repository databases and Localized Diabetes Dataset (LDD) has been gathered from Bombay Medical Hall, Upper Bazar Ranchi, Jharkhand, India. In the second phase, the dataset has been processed through two different approaches. The first approach entails classification through Adaboost, Classification via Regression (CVR), Radial Basis Function Network (RBFN), K-Nearest Neighbor (KNN) on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been applied as a feature reduction method followed by using the same set of classification methods used in the first approach. Among all of the implemented classification methods, PCA_CVR achieves the maximum performance for both the above mentioned datasets.Conclusion: In this article, comparative analysis of outcomes obtained by with and without the use of PCA and LDA for the same set of classification method has been done w.r.t performance assessment. Finally, it has been concluded that PCA & LDA both are useful to remove the insignificant features, decreasing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be applied to other medical diseases.

Cybersecurity has become an inevitable concern in the healthcare industry due to the rapid growth of the Internet of Health Things (IoHT). The IoHT is revolutionizing healthcare by enabling remote access to hospital equipment, real-time patient monitoring, and urgent alerts to patients and hospitals. However, the convenience of these systems also makes them vulnerable to cyberattacks, with hackers seeking to disrupt health services or extort money through ransomware attacks. Efficiently detecting multiple threats is a challenging task because IoHT generates large temporal data and system log information. In this article, we propose time-series classification models for the identification of potential cyberattacks in IoHT networks. First, we introduce neighborhood component analysis (NCA) with modifications of the regularization parameter to select the vital input features. With the selected features, we propose two LSTM-based models: 1) directed acyclic graph-based long short-term memory (DAG-LSTM) and 2) projected layer-based long short-term memory (PL-LSTM) for detecting cyberattacks. We evaluate the existing time-series classification models [i.e., gated recurrent unit (GRU), LSTM, and bilinear LSTM (Bi-LSTM)] and proposed models (i.e., DAG-LSTM and PL-LSTM) using real-world IoHT data. We also validate the models by applying a nonparametric statistical test, Friedman test. Our evaluation results show that the proposed DAG-LSTM achieves the highest accuracy with 99.89% training and 92.04% an average testing accuracy.

Smart farming influences advanced technologies to optimize agricultural procedures, yet it meets significant cybersecurity challenges, particularly in external intrusion detection (EID). This article proposes a novel architecture combining blockchain technology and federated learning (FL) to reinforce the security of smart farming systems (SMSs) against external threats. The integration of blockchain ensures data authentication and transparent data storage, while FL enables collaborative model training without compromising data privacy. Our architecture employs ensemble learning (EL) for the local model at the ensemble layer to train each smart land’s data and offers privacy-prevented security. These devices utilize FL techniques to collaboratively train intrusion detection models while preserving the confidentiality of sensitive data. The aggregated model completes data aggregation at the authentication layer, and the Proof of Authentication Consensus Algorithm is leveraged for smart land’s data authentication. The Internet of Things Sensor device’s identical information of smart lands is stored at the macro base stations (MBSs). After downloading the aggregated values of the aggregated model, the local model transfers the smart lands information to the Cloud layer for decision making and decentralized storage. The validation outcomes of the proposed architecture demonstrate excellent performance, with an average processing time of 3.663 s and 0.9956 accuracy for smart land compared to existing frameworks.

In the oil and gas IIoT environment, fire detection systems heavily depend on fire sensor data, which can be prone to inaccuracies due to faulty or unreliable sensors. These sensor issues, such as noise, missing values, outliers, sensor drift, and faulty readings, can lead to delayed or missed fire predictions, posing significant safety and operational risks in the oil and gas industrial IoT environment. This paper presents an approach for handling faulty sensors in edge servers within an IIoT environment to enhance the reliability and accuracy of fire prediction through multi-sensor fusion preprocessing, machine learning (ML)-driven probabilistic model adjustment, and uncertainty handling. First, a real-time anomaly detection and statistical assessment mechanism is employed to preprocess sensor data, filtering out faulty readings and normalizing data from multiple sensor types using dynamic thresholding, which adapts to sensor behavior in real-time. The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. A belief mass assignment mechanism is introduced, giving more weight to reliable sensors to ensure they have a stronger influence on fire detection. Simultaneously, a dynamic belief update strategy continuously adjusts sensor trust levels, reducing the impact of faulty readings over time. Additionally, uncertainty measurements using Hellinger and Deng entropy, along with Dempster–Shafer Theory, enable the integration of conflicting sensor inputs and enhance decision-making in fire detection. This approach improves decision-making by managing sensor discrepancies and provides a reliable solution for real-time fire predictions, even in the presence of faulty sensor readings, thereby mitigating the fire risks in IIoT environments.

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats.