Auto manufacturers and research groups are working on autonomous driving for long period and achieved significant progress. Autonomous vehicles (AV) are expected to transform road traffic reduction from current conditions, avoiding accidents and congestion. As the implementation of an autonomous vehicle ecosystem includes complex automotive technology, ethics, passenger behaviour, traffic management policies and liability etc., the maturity of AV solutions are still evolving. The proposed model to improve AV user experience, uses a hybrid AV Network of multiple connected autonomous vehicles which communicate with each other in an environment shared by human driven vehicles. The proposed Optimal AV Network (OAVN) solution provides better coordination and optimization of autonomous vehicles, improved Transportation efficiency, improved passenger comfort and safety, real-time dynamic adaption of traffic road conditions along with improved in-cabin assistance with inputs from various sensors. The true optimal solution for this problem, is to devise an automated guidance system for vehicles in an AV network, to reach destinations in best possible routes along with passenger comfort and safety. A custom informed search model is proposed along with other heuristic goals for better user experience. The results are analysed and compared to evaluate the effectiveness of the solution and identify gaps and future enhancements.

Community parks play a crucial role in promoting physical activity and overall well-being. This study introduces DLICP (Deep Learning Integrated Community Parks), an innovative approach that combines deep learning techniques specifically, face recognition technology with a novel walking activity measurement algorithm to enhance user experience in community parks. The DLICP utilizes a camera with face recognition software to accurately identify and track park users. Simultaneously, a walking activity measurement algorithm calculates parameters such as the average pace and calories burned, tailored to individual attributes. Extensive evaluations confirm the precision of DLICP, with a Mean Absolute Error (MAE) of 5.64 cal and a Mean Percentage Error (MPE) of 1.96

The maintenance, archiving and usage of the design drawings is cumbersome in physical form in different industries for longer period. It is hard to extract information by simple scanning of drawing sheets. Converting them to their digital formats such as Computer-Aided Design (CAD), with needed knowledge extraction can solve this problem. The conversion of these machine drawings to its digital form is a crucial challenge which requires advanced techniques. This research proposes an innovative methodology utilizing Deep Learning methods. The approach employs object detection model, such as Yolov7, Faster R-CNN, to detect physical drawing objects present in the images followed by, edge detection algorithms such as canny filter to extract and refine the identified lines from the drawing region and curve detection techniques to detect circle. Also ornaments (complex shapes) within the drawings are extracted. To ensure comprehensive conversion, an Optical Character Recognition (OCR) tool is integrated to identify and extract the text elements from the drawings. The extracted data which includes the lines, shapes and text is consolidated and stored in a structured comma separated values(.csv) file format. The accuracy and the efficiency of conversion is evaluated. Through this, conversion can be automated to help organizations enhance their productivity, facilitate seamless collaborations and preserve valuable design information in a digital format easily accessible. Overall, this study contributes to the advancement of CAD conversions, providing accurate results from the translating process. Future research can focus on handling diverse drawing types, enhanced accuracy in shape and line detection and extraction.

The cancer disease prediction and detection processes are crucial tasks in this emerging world and it is tough to manage the diseases. Generally, the disease prediction processes are done by using various kinds of inputs including numerical data and Magnetic Resonance Imaging (MRI) images. In the past, the researchers use the segmentation and classification methods on MRI images to enrich performance of the disease prediction system. However, these input images are using for the prediction, detection and diagnosis with reasonable delay and less prediction accuracy. In this work, we propose a new brain tumor disease prediction and diagnosis system that incorporates the segmentation and classification techniques for predicting and detecting the cancer diseases effectively. Here, new data pre-processing methods like mean fusion, automated cropping of the Region of Interest (ROI), and Gaussian filtering are used for performing effective classification. Moreover, the proposed system performs the standardized resizing and rescaling processes over the MRI images and it also automate the cropping process of the ROI to ensure consistent input sizes. In addition, the brain tumor segmentation process is performed by applying the LinkNet architecture with a SEResNeXt101 backbone network. Finally, the system focuses on brain tumor classification using ensemble model which combines the ResNet architectures through stacking, leveraging the strengths of individual models to improve classification accuracy in the first stage, and it incorporates the XGBoost algorithm to enhance the performance further in efficient manner. The proposed disease prediction system is assessed through experiments on MRI images. Finally, the system is proved as superior to other available disease prediction systems with respect to sensitivity, specificity, AUC and obtained 95.84% as overall prediction accuracy.

Web phishing poses a dynamic threat, requiring detection systems to quickly adapt to the latest tactics. Traditional approaches of accumulating data and periodically retraining models are outpaced. We propose a novel paradigm combining federated learning and continual learning, enabling distributed nodes to continually update models on streams of new phishing data, without accumulating data. These locally adapted models are then aggregated at a central server via federated learning. To enhance detection, we introduce a custom attention-based classifier model with residual connections, tailored for web phishing, leveraging attention mechanisms to capture intricate phishing patterns. We evaluate our hybrid learning paradigm across continual learning strategies (cumulative, replay, MIR, LwF) and model architectures through an empirical investigation. Our main contributions are: (1) a new hybrid federated-continual learning paradigm for robust web phishing detection, and (2) a novel attention + residual connections based model explicitly designed for this task, attaining 0.93 accuracy, 0.90 precision, 0.96 recall and 0.93 f1-score with the LwF strategy, outperforming traditional approaches in detecting emerging phishing threats while retaining past knowledge.

Bluetooth Low Energy (BLE) is a wireless communication technology that allows for low-power, short-distance data transmission between devices. Our research presents an investigation into the potential applications of Bluetooth Low Energy (BLE) communication technology in proximity sensing and access control. The primary focus of this study is to leverage the path loss model and Multilayer Perceptron (MLP) algorithm for dynamically predicting path loss exponent variations caused by changes in environmental factors such as temperature and humidity. The experimental setup uses the Arduino Nano BLE 33 Sense and iPhone 13 Pro devices to establish wireless communication and conduct proximity detection tests. The evaluation of our technique for proximity detection is carried out in varied environments, including an anechoic chamber and a classroom. Our results demonstrate that the proposed approach can effectively identify device proximity and provide access control. The implications of these findings could be instrumental in enhancing the security and efficiency of wireless communication in a range of applications, including home automation, healthcare, and industry 4.0.