In this study, a millimetre‐wave (MMW) antenna is presented for the fifth‐generation (5G) wireless multiple‐input multiple‐output (MIMO) applications, in order to offer numerous advantages including compactness, planar geometry, high bandwidth, and high gain performance. The concept of defected ground structures has been deployed for the first time in MIMO antenna design at MMW spectrum to fulfil 5G requirements of high bandwidth with compactness and low design complexity. The top surface of antenna comprises of a coplanar waveguide‐fed T‐shaped radiating patch element, while the bottom part is designed to constitute a partial ground loaded with two iterations of symmetrical split‐ring slots at an optimised distance. Experimental results depict a wide bandwidth of 25.1–37.5 GHz, as well as a peak gain of 10.6 dBi at 36 GHz. Moreover, numerically evaluated efficiency of >80% is observed over the entire intended bandwidth of operation. For the demonstration, four‐element MIMO antenna array of the proposed antenna element is also fabricated and tested, in order to validate high isolation between the adjacent elements, which makes the proposed antenna a potential candidate to be integrated in cellular phones and base stations for 5G MIMO systems and services.

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

This paper presents the design, fabrication and performance evaluation of a flexible millimeter-wave (mm-wave) antenna array for the fifth generation (5G) wireless networks operating at Ka-band (26.5-40 GHz). The single element antenna is comprised of a coplanar-waveguide (CPW)-fed rectangular patch tapered at its sides with two vertically oriented slots. The ground is designed with L-shaped stubs to converge the dispersed radiation pattern for improving the directivity and gain. The antenna fabrication is accomplished by two advanced methods of laser-milling and inkjet printing on a thin film of flexible liquid crystal polymer (LCP). A novel and time-efficient method for post-printing sintering is also proposed in this work. The design is extended in a two-element array for the gain enhancement. Measurements have validated that the proposed antenna array exhibits a bandwidth of 26-40 GHz with a peak gain of 11.35 dBi at 35 GHz, and consistent high gain profile of above 9 dBi in the complete Ka-band. These features recommend the proposed antenna array as an efficient solution for integration in future flexible 5G front-ends and mm-wave wearable devices.

This letter presents a novel and compact antenna array suggesting geometrical modifications in the concept of collinear arrays at millimeter-wave (MMW) spectrum. The extension of the standard narrowband Franklin linear antenna array in a compact 2-D assembly is proposed and multiband response is accomplished in this work, which fulfills demands of future fifth-generation (5G) systems. The proposed 2-D antenna array is designed to operate at licensed frequency bands of 28 and 37-39 GHz recommended by the U.S. Federal Communications Commission for 5G wireless networks, as well as at 33 GHz for satellite communication and navigation applications. Measurements validate the triple-band response at the desired frequencies of operation. The proposed antenna array also offers distinguishing performance in terms of high gain and significant efficiency above 70% in all the MMW operating bands.

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

Rapid urbanization and industrialization in Lahore and Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO ₂ ) concentrations, land surface temperature (LST), and vegetation. The study aims to comprehensively assess changes in NO ₂ , LST, and vegetation induced by industrialization, focusing on seasonal variations from 2019 to 2022. The study evaluates NO ₂ concentrations vegetation health using indices normalized difference vegetation index, enhanced vegetation index, atmospherically resistant vegetation index, and LST variations. The analysis reveals a notable increase in NO ₂ during both summer and winter, with approximately 0.021 (×10 ³ mol/m ² ) and 0.03 (×10 ³ mol/m ² ) rises observed in Lahore. In comparison, Faisalabad experienced more modest increases of around 0.0034 (×10 ³ mol/m ² ) and 0.007 (×10 ³ mol/m ² ) in the respective seasons. Simultaneously, vegetation indices decline in both cities, indicating substantial vegetation health deterioration. Moreover, a notable upward trend in LST occurred, with Lahore experiencing an increase of approximately 1.59 °C in summer and 0.92 °C in winter. Faisalabad also showed rises of around 1.64 and 0.54 °C in the corresponding seasons. Pearson correlation analysis highlights a robust negative correlation between NO ₂ and vegetation indices, underlining the impact of declining vegetation on air quality. A positive correlation between NO ₂ and LST indicates the interconnected nature of rising temperatures and air pollution. The findings emphasize the need for environmental regulations in Lahore and Faisalabad. Addressing rising NO ₂ levels and temperatures is critical for policymakers and urban planners. These insights contribute to the sustainable development goal 11, fostering strategies for sustainable cities and communities to combat pressing environmental challenges in these urban areas.