With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.

Object recognition in depth images is challenging and persistent task in machine vision, robotics, and automation of sustainability. Object recognition tasks are a challenging part of various multimedia technologies for video surveillance, human–computer interaction, robotic navigation, drone targeting, tourist guidance, and medical diagnostics. However, the symmetry that exists in real-world objects plays a significant role in perception and recognition of objects in both humans and machines. With advances in depth sensor technology, numerous researchers have recently proposed RGB-D object recognition techniques. In this paper, we introduce a sustainable object recognition framework that is consistent despite any change in the environment, and can recognize and analyze RGB-D objects in complex indoor scenarios. Firstly, after acquiring a depth image, the point cloud and the depth maps are extracted to obtain the planes. Then, the plane fitting model and the proposed modified maximum likelihood estimation sampling consensus (MMLESAC) are applied as a segmentation process. Then, depth kernel descriptors (DKDES) over segmented objects are computed for single and multiple object scenarios separately. These DKDES are subsequently carried forward to isometric mapping (IsoMap) for feature space reduction. Finally, the reduced feature vector is forwarded to a kernel sliding perceptron (KSP) for the recognition of objects. Three datasets are used to evaluate four different experiments by employing a cross-validation scheme to validate the proposed model. The experimental results over RGB-D object, RGB-D scene, and NYUDv1 datasets demonstrate overall accuracies of 92.2%, 88.5%, and 90.5% respectively. These results outperform existing state-of-the-art methods and verify the suitability of the method.

The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), have made outstanding accomplishments. Deep feature extraction from a CNN model is a frequently utilized technique in these approaches. Although CNN-based techniques have achieved considerable success, there is indeed ample space for improvement in terms of their classification accuracies. Certainly, fusion with other features has the potential to extensively improve the performance of distant imaging scene classification. This paper, thus, offers an effective hybrid model that is based on the concept of feature-level fusion. We use the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images. The segmented regions of the image are then labeled using a Markov random field (MRF). After the segmentation and labeling of the objects, classical and CNN features are extracted and combined to classify the objects. After categorizing the objects, object-to-object relations are studied. Finally, these objects are transmitted to a fully convolutional network (FCN) for scene classification along with their relationship triplets. The experimental evaluation of three publicly available standard datasets reveals the phenomenal performance of the proposed system.

As the cyber threats rise, and IT organization solutions become more complex, the signature-based or the perimeter solutions proved themselves insufficient or ineffective. In this research, Zero-Trust Network Access (ZTNA) deployment is examined in hybrid environments and can be analyzed and discussed within three major contexts: compliance, identity, and security measures. Conducting a quantitative study, the research aimed to assess security performance data from 12 organizations collected over the 24-month period, pre-and post-implementation of ZTNA. The study presented several strengths and increases by showing the effectiveness of the proposed solution: a 46% decrease of access violations, a 44% decline in authentication failures, a 63% decrease of the length of time threats remained undetected, and a 67% increase of response time to an alerted threat. These outcomes clearly depict the utility of ZTNA in minimizing lateral movement, improving the concept of adaptive authentication, and further positioning it as an ideal solution to support micro-segmentation to contain threats before they can spread further. With the federation of identity, behaviour, and contextual access controls, ZTNA presents comprehensive security that meets the requirements of modern enterprises and their systems. This paper not only asserts the efficiency of the Zero-Trust concepts but also outlines practical recommendations for organizations to adopt from traditional security models to more flexible and customizable identity-based approach to network authorization.

This study examined the transformative potential of Data-Centric Artificial Intelligence (DCAI) in improving model performance through quality-driven data engineering. Moving beyond algorithmic refinement, the research focused on how enhanced data quality—achieved through systematic cleaning, annotation, augmentation, and validation—contributed to the robustness, fairness, and interpretability of AI models. A mixed-methods approach was employed, combining experimental evaluation of machine learning models across multiple datasets with qualitative insights into data governance and lifecycle management. The findings revealed that high-quality data substantially increased predictive accuracy, reduced overfitting, and improved model generalization across domains. Moreover, the integration of automated data validation tools and human-in-the-loop systems enhanced dataset reliability and minimized bias. The study also emphasized the importance of ethical data sourcing and transparency, aligning with emerging global AI governance frameworks. It concluded that prioritizing data quality over algorithmic complexity produced more sustainable and trustworthy AI systems. The research provided actionable recommendations for embedding data governance, automation, and interdisciplinary collaboration within AI pipelines. Future directions included developing standardized data quality metrics, exploring explainable AI integration, and leveraging federated learning and synthetic data to scale data-centric frameworks. This paradigm shift positioned data as the foundational element driving the next generation of reliable and ethical artificial intelligence.