Abir Hossain

Intelligent reflecting surfaces (IRS) have emerged as a transformative technology for B5G/6G networks, offering the ability to actively reshape the wireless propagation environment and support reliable line‐of‐sight communication. In the context of intelligent transportation systems, however, meeting the stringent reliability and latency demands of ultra‐reliable low latency communication (URLLC) remains a significant challenge, particularly under packet collision scenarios. To address this, we propose a retransmission‐based protocol called retransmission mechanism with reserved bandwidth in IRS (RMRI), tailored to satisfy URLLC requirements in IRS‐assisted vehicular networks. RMRI leverages a finite set of reserved bandwidth channels to instantly retransmit collided packets, combined with a packet diversity principle using 3‐, 5‐, and 7‐packet duplication and a slotted Aloha mechanism operating within 0.2 ms time slots. We evaluate the protocol in terms of the minimum subcarriers needed to achieve 99.9999% reliability within a 0.2 ms latency threshold, maximum supported users, end‐to‐end latency, and path loss using the 3GPP UMi model. Results show that RMRI consistently outperforms conventional schemes, offering reduced subcarrier usage, enhanced reliability, and low latency, effectively meeting the demands of next‐generation intelligent transportation systems.

As network‐based services continue to expand and cyber threats grow increasingly sophisticated, the deployment of effective network intrusion detection systems (NIDS) has become crucial for the protection of sensitive data and the preservation of digital infrastructure integrity. These systems are indispensable in ensuring the security and reliability of digital environments. This study presents an all‐encompassing approach that integrates feature selection, ensemble learning, clustering, and diverse analytical methods to improve the precision and effectiveness of network intrusion detection systems. Using data sets that have been preprocessed and classified using the Random Forest and XGBoost algorithms, we extract key features and categorize key variables using K‐means clustering, thus constructing a robust feature set for intrusion detection. Our experimental results demonstrate high precision in detecting benign and malicious activities using the CIC‐IDS‐17 and UNSW‐NB15 datasets. These outcomes underscore the efficacy of our proposed approach in monitoring network traffic, effectively identifying threats, and preventing potential intrusions.

Background: Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. Brain tumors can be broadly classified into two categories: primary tumors, which originate within the brain, and secondary tumors, which are metastatic in nature. Effective glioma, meningioma, and pituitary tumor diagnosis and treatment requires the precise differentiation of these tumors as well as non-tumors for improved clinical outcomes. Methods: Here, we present a new method to classify brain tumors based on the MobileNetV2 architecture with advanced preprocessing for high accuracy. We accessed an MRI image dataset from Kaggle that contained 1311 images in the test set. We split the data into 80% training and 20% testing. All images underwent extensive preprocessing, including grayscale conversion, noise removal, and contrast-limited-adaptive-histogram equalization (CLAHE). All images were resized to 224 × 224 pixels. Using transfer learning, the baseline frozen layers were kept intact while the top layers were trained with a learning rate of 0.0001, which was tuned to the model’s requirements using early stopping to avoid overfitting. Results: With the outlined methodology, we obtained an astounding accuracy of 99.16%, including strong performance in the no-tumor category, where recall rates were approaching 100% and false positive rates were minimized. Conclusions: These findings strongly indicate that the application of lightweight convolutional neural networks in diagnostic imaging can considerably expedite accurate brain tumor identification by radiologists.

Schizophrenia is a debilitating and persistent mental disorder. It manifests as cognitive, emotional, and behavioral limitations with various degrees of social function impairment that get worse with time. To identify those limitations, a deep learning-based neuroimaging technique is feasible to detect. In the last two decades, neuroimaging, precisely functional magnetic resonance imaging (fMRI) has become indispensable for investigating the brain image processing and related abnormalities of schizophrenia patients. We proposed a modified Convolutional Neural Network (CNN) model based on VGG16. Along with our proposed model, a set of CNN models—VGG16, VGG19, EfficientNet, Inception V3, and ResNet-50 have been considered to identify schizophrenia and healthy controls. We evaluated correlation analysis on time series feature extractions over Open-NEURO clinical dataset and achieved accuracy of schizophrenia detection of 99.5%. Moreover, Receiver Operating characteristic Curve (ROC) have been generated to compare with existing reference models. This research significantly affects the primary detection of schizophrenia and might prevent a patient from mental disorder at an early stage.

Intra-vehicular communication is an emerging technology explored spontaneously due to higher wireless sensor-based application demands. To meet the upcoming market demands, the current intra-vehicular communication transmission reliability and latency should be improved significantly to fit with the existing 5G and upcoming 6G communication domains. Ultra-Reliable Low-Latency Communication (URLLC) can be widely used to enhance the quality of communication and services of 5G and beyond. The 5G URLLC service is highly dependable for transmission reliability and minimizing data transmission latency. In this paper, a multiple-access scheme named Cluster-based Orthogonal Frequency Subcarrier-based Multiple Access (C-OFSMA) is proposed with 5G URLLC’s high requirement adaptation for intra-vehicular data transmission. The URLLC demanded high reliability of approximately 99.999% of the data transmission within the extremely short latency of less than 1 ms. C-OFSMA enhanced the transmission diversity, which secured more successful data transmission to fulfill these high requirements and adapt to such a network environment. In C-OFSMA, the available sensors transmit data over specific frequency channels where frequency selection is random and special sensors (audio and video) transmit data over dedicated frequency channels. The minimum number of subcarrier channels was evaluated for different arrival rates and different packet duplication conditions in order to achieve 99.999% reliability within an air-interface latency of 0.2 ms. For the fixed frequency channel condition, C-OFSMA and OFSMA were compared in terms of reliability response and other packet duplication. Moreover, the optimal number of clusters was also evaluated in the aspects of the reliability response for the C-OFSMA system.

Ultra-Reliable Low-Latency Communication (URLLC) is challenging due to its extremely higher reliability requirement with stringent short latency packet transmission. In order to overcome this reliability and latency bound, a communication access scheme needs to assure almost error-free and high speed packet transmission. In this paper, a new multiple-access scheme—Orthogonal Frequency-Subcarrier-based Multiple Access (OFSMA)—is proposed with URLLC’s high requirement adaptation. In this scheme, the packet diversity concept is incorporated to achieve the expected packet transmission reliability and a diverse number of the duplicated packet are processed with a set of operations and transmitted over randomly selected orthogonal subcarrier frequency channels. Performances of the OFSMA system are measured in terms of applying several numbers of frequency bands, a massive number of subcarrier channels, a different number of packet duplications, and a diverse rate of traffic arrival conditions. We determined the minimum number of subcarrier channels requirement to satisfy the reliability of 99.999% for different packet duplication in presence of different frequency bands. The reliability response for a fixed number of subcarrier channels is evaluated for different frequency band conditions. Finally, the air interface latency of the OFSMA system is measured for single packet uplink transmission and compared with that of a traditional OFDMA system. The performance results in terms of reliability and latency express that the OFSMA scheme can assure the expected reliability and latency defined by URLLC.