In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

A reliable air quality prediction model is required for pollution control, human health monitoring, and sustainability. The existing air quality prediction models lack efficiency due to overfitting in prediction model and local optima trap in feature selection. This study proposes the Balanced Spider Monkey Optimization (BSMO) technique for effective feature selection to overcome the local optima trap and overfitting problems. The air quality prediction data were collected from the Central Pollution Control Board (CPCB) from four cities in India: Bangalore, Chennai, Hyderabad, and Cochin. Normalization is performed using Min-Max Normalization and fills the missing values in the dataset. A Convolutional Neural Network (CNN) is applied to provide deep representation of the input dataset. The BSMO technique selects the relevant features based on the balancing factor and provides the relevant features for the Bi-directional Long Short-Term Memory (Bi-LSTM) model. The Bi-LSTM model provides the time series prediction of air quality for four cities. The BSMO model obtained higher feature selection performance compared to existing techniques in air quality prediction. The BSMO-BILSTM model obtained 0.318 MSE, 0.564 RMSE, and 0.224 MAE, whereas Attention LSTM reached 0.699 MSE, 0.836 RMSE, and 0.892 MAE. Our solution may be of particular interest to various governmental and non-governmental institutions focused on maintaining high Quality of Life (QoL) on the local or state level.

The rapid proliferation of Internet-connected devices has elevated the significance of cybersecurity, making intrusion detection a critical aspect of maintaining network integrity. Traditional security measures often fail to provide adequate protection against sophisticated attacks, necessitating advanced and robust solutions. This paper introduces a comprehensive cyber-internet security framework that leverages machine learning techniques for real-time intrusion detection and prevention. The proposed methodology employs a hybrid approach, integrating supervised and unsupervised learning models to detect anomalies and classify intrusions effectively. Specifically, a combination of Support Vector Machine (SVM), Decision Trees (DT), and K-means clustering is used to enhance detection accuracy and reduce false-positive rates. The experimental results demonstrate that the proposed model achieved a detection accuracy of 97.8%, a precision of 96.5%, and a recall of 95.2% on the NSL-KDD dataset. The implementation also reduced the false-positive rate to 1.2% and the computational overhead by 15% compared to traditional detection systems. Additionally, the proposed system was tested on real-time traffic data, where it successfully identified and mitigated various cyber threats, including Distributed Denial of Service (DDoS) attacks and network infiltrations, with minimal latency and high reliability. In conclusion, the study presents an efficient and secured cyber-internet security framework that significantly enhances intrusion detection capabilities using machine learning techniques. The proposed system provides a scalable and adaptive solution for securing critical infrastructure and networks against evolving cyber threats, making it an ideal candidate for deployment in real-world cybersecurity applications.

Smart cities are equipped with a vast number of IoT devices, which help to collect and analyze data to improve the quality of life for urban people by offering a sustainable and connected environment. However, the rapid growth of IoT systems has issues related to the Quality of Service (QoS) and allocation of limited resources in IoT-based smart cities. The cloud in the IoT system also faces issues related to higher consumption of energy and extended latency. This research presents an effort to overcome these challenges by introducing opposition-based learning incorporated into Golden Jackal Optimization (OL-GJO) to assign distributed edge capabilities to diminish the energy consumption and delay in IoT-based smart cities. In the context of IoT-based smart cities, a three-layered architecture is developed, comprising the IoT system, the Unmanned Aerial Vehicle (UAV)-assisted edge layer, and the cloud layer. Moreover, the controller positioned at the edge of UAV helps determine the number of tasks. The proposed approach, based on opposition-based learning, is put forth to offer effective computing resources for delay-sensitive tasks. The multi-joint symmetric optimization uses OL-GJO, where opposition-based learning confirms a symmetric search process is employed, improving the task scheduling process in UAV-assisted edge computing. The experimental findings exhibit that OL-GJO performs in an effective manner while offloading resources. For 200 tasks, the delay experienced by OL-GJO is 2.95 ms, whereas Multi Particle Swarm Optimization (M-PSO) and the auction-based approach experience delays of 7.19 ms and 3.78 ms, respectively.

In this study, a TiN-Ga₂O₃ composite thin films were deposited using RF magnetron co-sputtering and investigated for its structural characteristics and pH-sensing capabilities. The composite material was integrated into an Extended Gate Field-Effect Transistor (EGFET) pH sensor, which was evaluated for its sensitivity, linearity, and stability. The fabricated sensor demonstrated a voltage sensitivity of 59.4 mV/pH, approaching the theoretical Nernstian limit, demonstrating high pH sensitivity and electrochemical stability. The device exhibited an exceptional linearity (R²) of 0.997, confirming its accuracy in pH detection over a broad pH range from 2 to 10. Minimal hysteresis of 3.2 mV was recorded, indicating high repeatability and stability under varying measurement conditions. The structural analysis of the TiN-Ga₂O₃ film revealed a uniform surface morphology, which contributed to enhanced sensing performance. The presence of Ga₂O₃ improved the chemical stability and surface reactivity of the film, while TiN provided high conductivity and durability. The synergy between these two materials resulted in improved charge transfer efficiency, which is crucial for reliable pH sensing. The wide pH detection range makes this sensor suitable for various fields, including biomedical diagnostics, environmental monitoring, and industrial process control.