For a precise diagnosis and treatment plan to be planned in a clinical setting, brain tumour categorization from magnetic resonance imaging (MRI) data is essential. Clinicians can efficiently monitor the course of the disease, select the best course of treatment, and gauge the effectiveness of that course of treatment with the help of fast and accurate tumour classification. Additionally, machine learning models can help radiologists make better diagnoses by lowering interpretation errors and enhancing the quality of MRI image interpretation. Classifying brain tumours also aids in research endeavours to discover biomarkers, comprehend the biology of tumours, and create tailored treatments for various tumour subtypes. Current techniques for classifying tumours in the brain from MRI data frequently rely on labour-intensive, inconsistent manual segmentation and feature extraction. These techniques might not be able to accurately classify tumours due to minute variations in their morphology or texture. Furthermore, the accessibility and scalability of traditional procedures in clinical settings may be limited due to the need for expertise in radiology and medical imaging. Furthermore, manual feature engineering could miss significant tumour traits or not fully utilize MRI data for categorization. To overcome the shortcomings of current approaches, the suggested system makes use of machine learning techniques to improve and automate the classification of brain tumours using MRI image data. In order to extract discriminative features directly from MRI scans, this work uses machine learning algorithms. The proposed models are capable of reliably classifying brain cancers into important categories and effectively differentiating between different types of tumours by training them on large-scale MRI datasets labelled with tumour labels.

The importance of obtaining details regarding transportation vehicles has been increasing in developing countries such as India. Object detection plays an important role in the automatic identification of the content of an image or video without human intervention. Various deep learning models have been developed for object detection using CNNs (convolution neural networks). This paper proposes a method for vehicle detection from still images using an optimized YOLO-NAS (You Only Look Once-Neural Architecture Search) frame work. This model is verified with earlier YOLO models for improved accuracy and optimization. The experiments were conducted on two publicly available datasets. Indian Driving Dataset (IDD) and DATS_2022 having exclusively images of various traffic scenes on Indian roads. The proposed method out performs the existing object detection models in terms of detection accuracy. Results show that the proposed method is good at detection accuracy measured in Average Precision and Recall.

Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) communication, allowing high data rates and low latency, is a crucial enabler for 5G and beyond wireless networks. However, its sensitivity to eavesdropping and physical-layer attacks poses serious security challenges. This research proposes a novel framework that enhances physical layer security in MIMO by integrating deep learning–based key generation and federated learning–assisted secure transmission. Capitalizing on the spatial uniqueness of mmWave propagation, we employ a cross-layer mobilenetV2 pyramid mutual attention network to extract rich physical-layer features—such as angle of arrival and angle of departure, for robust key generation without requiring prior key exchange. To enhance the security of the transmission process, we introduce a multichannel squeeze-and-excitation combined network trained in a federated learning setting, allowing distributed devices to jointly learn a secure transmission model without compromising data privacy. Experimental results demonstrate that the proposed method significantly enhances resistance to passive eavesdropping and ensures low bit error rates, even under dynamic channel conditions and high secrecy rate, key generation rate of about 1.63 at 40 dB signal-to-noise ratio and bit error rate of 0.1 at 25 dB. The proposed approach enables lightweight, scalable, and privacy-preserving security in mmWave-enabled communication systems.

Automatic number plate recognition (ANPR) plays a major role in real life applications and several techniques have been proposed. The localization or detection of the number plate of the vehicle images is the basis for any ANPR system. This paper proposes a robust method for localization of number plates in different conditions. There are two stages; first the preprocessing of the input image is performed and then localization is done. After preprocessing the statistical measures such as root mean square error and peak signal to noise ratio are calculated. Next the localization is done using symmetric wavelets and mathematical morphology. Experimental results show that this method gives dominant values of RMSE and PSNR. Experiments were performed on a database and also on a sample of 280 images of different countries taken from various scenes and conditions; results show that success rate of 77.14% on database and 92.14% on sample images achieved.