Ajit Danti

The designed method introduces a novel approach for road humps detection and distance estimation to promote the safety of intelligent transportation systems (ITS) under low-light conditions and congested traffic. The key contribution of this work is to integrate CLAHE-based contrast enhancement and a lightweight MobileNet backbone into a faster R-CNN architecture, which improves feature extraction efficiency without sacrificing accuracy. After the hump is detected, the distance to the hump is estimated using stereo vision, which computes disparity and uses triangulation to determine real-world depth. Unlike existing methods, the proposed approach combines road hump detection with distance estimation using CLAHE for low-light image enhancement, allowing for more accurate and dependable perception in difficult lighting situations. In contrast to traditional techniques, our method allows for the accurate recognition of both marked and unmarked humps in a variety of road and lighting conditions, including background clutter and occlusions. Experiments were conducted on an expanded Mendeley dataset and real-world driving scenarios. The proposed model outperformed the baseline faster R-CNN and YOLO variations with a 94% detection accuracy. The overall root mean square error (RMSE) for the distance estimation was 0.62 m. The major advantage of this system is the improvement of transport management, autonomous vehicles, and especially for safe driving.

This chapter explores how Artificial Intelligence can reshape net-zero cities by enabling smart infrastructure and sustainable urban development. It explains how AI-driven innovations can optimize urban systems, reduce carbon emissions, and accelerate progress toward global climate goals. Key focus areas include intelligent energy management, sustainable mobility, and resource-efficient urban planning as core elements of net-zero strategies. The chapter emphasizes the integration of modeling and simulation with AI to design, evaluate, and validate sustainable city systems. Through forecasting, optimization, and real-time analytics, planners can simulate scenarios, assess trade-offs, and implement scalable solutions. Advances in sensor data, machine learning, and reinforcement learning further support predictive maintenance and adaptive decision-making, positioning AI as a catalyst for achieving carbon-neutral cities.

The rapid growth of mobile payment platforms has enhanced transactional convenience but also introduced critical security challenges, notably shoulder spoofing. This attack occurs when unauthorized individuals or surveillance devices visually intercept sensitive information, such as Mobile Personal Identification Numbers (MPINs), during payment processes. Existing security mechanisms—including PIN masking and screen dimming—fail to detect environmental threats or provide adaptive responses, leaving users vulnerable in public spaces. To address this gap, propose a novel solution titled Gaze-Aware Threat Detection with Contextual Scene Analysis (GATCSA). GATCSA leverages the front-facing camera and on-device computer vision algorithms to monitor the surroundings during mobile transactions. The system identifies suspicious behavior such as gaze fixation by nearby individuals or the presence of surveillance equipment targeting the mobile screen. A risk evaluation module considers proximity, gaze direction, and focus duration to classify threat levels in real time. Upon detection, the system provides users with contextual alerts and actionable suggestions—such as changing the device angle, enabling a privacy screen, or halting the transaction—to safeguard against unauthorized visual access. Unlike traditional methods, GATCSA processes all data locally to ensure user privacy and operates efficiently on resource-constrained mobile devices. Preliminary testing in varied real-world conditions—differing in lighting, crowd density, and device orientation—demonstrates high accuracy in threat identification and user responsiveness. By integrating gaze tracking with environmental awareness, GATCSA represents a significant advancement in mobile payment security, enhancing user trust and privacy during digital transactions.

In an advanced intelligent transportation system vehicle recognition and classi f ication is very significant. In current research trend, recognition of vehicles is done byusingmachinelearning (ML)andcomputervisiontechniques. Vehicle’s multi-view images or videos with different lighting conditions are annotated and given to the deep neural network to build an automated system to recognize the vehicles models. The augmentation of data can increase the number of sam ples in learning, with the small available datasets. Geometric transformations, brightness changes, and different filter operations are applied to the data through data augmentation. Furthermore, be orthogonal experiments we determine the optimal data augmentation method to obtain 96% accuracy in results. Detailed information is reported based on the classification of four different types of vehi cles and the results show that convolutional neural network with 16 layers deep techniques are effective in solving challenging tasks while recognizing moving vehicles.

The swift advancement of the digital economy requires a recruitment system that is safe, effective, and easy to follow. This chapter describes the Blockchain-Enabled Smart Hiring System, which modernizes the hiring process by replacing traditional methods with Blockchain technology, smart contracts, and decentralized arbitration. This model helps reduce fraud attempts, protects data, and automates the resolution of conflicts by harnessing blockchain's immutability and security. This research provides an in-depth study regarding the architecture of BEASH, implementation, and performance evaluation. The study's results revealed higher efficiency, greater stakeholder trust, and less time for conflict resolution. Although scalability and regulatory compliance continue to be challenged, this approach demonstrates great promise for advances in recruitment and further development of digital hiring systems.

This research delves into the technical advancements of image segmentation and classification models, specifically the refined Pix2Pix and Vision Transformer (ViT) architectures, for the crucial task of osteoporosis detection using X-ray images. The improved Pix2Pix model demonstrates noteworthy strides in image segmentation, achieving a specificity of 97.24% and excelling in the reduction of false positives. Simultaneously, the modified ViT models, especially the MViT-B/16 variant, exhibit superior accuracy at 96.01% in classifying osteoporosis cases, showcasing their proficiency in identifying critical medical conditions. These models are poised to revolutionize osteoporosis diagnosis, providing clinicians with accurate tools for early detection and intervention. The synergies between the Pix2Pix and ViT models open avenues for nuanced approaches in automated diagnostic systems, with the potential to significantly improve clinical results and contribute to the broader landscape of medical image analysis. As osteoporosis remains a prevalent and often undiagnosed condition, the technical insights from this study hold substantial importance in advancing the field, emphasizing the critical role of accurate diagnostic tools in improving patient care and health outcomes.

Accurate tools are required to acknowledge misleading images in order to maintain image legitimacy, and these tools must allow for legal operations on images. Additionally, after posting their images to the Internet, image owners lose rights over the images because there are no measures in place to safeguard them from misuse. One of the most well-liked techniques for addressing copyright disputes is the use of steganography technologies. The embedded steganography images can, sadly, be easily altered or deleted. To address this problem, this work presents the spiking neural network (SNN) with blockchain for tampered image detection utilizing forensic steganography images. Forensic steganography images that have been altered can be found with this SNN. Using steganography images from the database, SNN is trained in this model. The blockchain stores the owners’ access policies. The Python platform is used to implement the proposed strategy. F-measure, specificity, accuracy, precision, recall false positive rate (FPR), and false negative rate (FNR) are used to gauge how well the proposed approach performs. When compared to state-of-the-art approaches, the proposed approach obtained an impressive rise of 98.65%, in classification accuracy.