Fog computing extends cloud capabilities to the network edge, aiding IoT and users. It mitigates cloud issues like latency and reliability. However, fog's limited resources pose security vulnerabilities like data theft and unauthorised access. To tackle this, we proposed a lightweight, energy-efficient hybrid cryptosystem with dynamic key changes. This optimises fog node energy usage and the proposed security system is collision proof and uses proportional offloading to ensure the delivery of subscribed data to fog/cloud. We have utilised chosen-ciphertext attribute-based encryption scheme to produce the keys and principles of ECC-Elgamal are considered for encryption/decryption of data. Finally, the performance evaluation and security attacks analysis depicts that our security system performs better in terms of optimal energy utilisation, overall computation overhead as well as mitigating the attacks in contrast with other state of art methodologies.

With a subtle rise in the vehicular traffic, it has become necessary for the users to be more aware of their surroundings. Vehicular AdHoc Network (VANET) provides a solution to connect these vehicles with the data centers to leverage this information for the benefit of the users. It renders end-to-end details for traffic flow monitoring and dynamic route scheduling of vehicles to prevent unforeseen incidents. Current researches suggest that numerous IoV architectures authenticate the vehicles enabling inter-vehicular communication. To improve the quality of service, this work leverage vehicle-to-roadside communication, and priority based batch-verification system. Further, to ensure safer computations of time-critical information and faster processing of event-driven messages, vehicular edge computing based hierarchical framework is proposed. The security analysis proves that this framework can substantially improve power consumption and mitigate security issues such as traceability, identity privacy-preserving, replay attacks, denial of service attacks, and impersonate attacks.

The eagle expresses of cloud computing plays a pivotal role in the development of technology. The aim is to solve in such a way that it will provide an optimized solution. The key role of allocating these efficient resources and making the algorithms for its time and cost optimization. The approach of the research is based on the rough set theory RST. RST is a great method for making a large difference in qualitative analysis situations. It's a technique to find knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, and data reduction. The rough set theory is the new method in cloud service selection so that the best services provide for cloud users and efficient service improvement for cloud providers. The simulation of the work is finished at intervals with the merchandise utilized for the formation of the philosophy framework. The simulation shows the IoT services provided by the IoT service supplier to the user are the best utilization with the parameters and ontology technique.

In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few-shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.

Performance of most of the recognition engines for document images is effected by quality of the image being processed and the selection of parameter values for the pre-processing algorithm. Usually the choice of such parameters is done empirically. In this paper, we propose a novel framework for automatic selection of optimal parameters for pre-processing algorithm by estimating the quality of the document image. Recognition accuracy can be used as a metric for document quality assessment. We learn filters that capture the script properties and degradation to predict recognition accuracy. An EM based framework has been formulated to iteratively learn optimal parameters for document image pre-processing. In the E-step, we estimate the expected accuracy using the current set of parameters and filters. In the M-step we compute parameters to maximize the expected recognition accuracy found in E-step. The experiments validate the efficacy of the proposed methodology for document image pre-processing applications.

With the availability of large collection of document images in Indian languages, image based retrieval has gained popularity. The performance of such systems is effected by the presence of degraded and noisy images. Moreover, Optical character recognition systems for Indian scripts are not yet robust, leading to noisy OCR'ed text. Information retrieval system designed using inputs from both modalities (image features and OCR based recognition data) will lead to better retrieval performance in contrast to usage of individual modality. In this paper we present a indexing methodology that uses multiple kernel learning to combine features from different modalities by joint optimization of search time and accuracy. The evaluation of the proposed methodology is demonstrated on document images of Bangla and Devanagari script.

Active learning and crowd sourcing are becoming increasingly popular in the machine learning community for fast and cost effective generation of labels for large volumes of data. However, such labels may be noisy. So, it becomes important to ignore the noisy labels for building of a good classifier. We propose a framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples. The approach inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance.

In this paper, we propose a novel framework for segmentation of documents with complex layouts. The document segmentation is performed by combination of clustering and conditional random fields (CRF) based modeling. The bottom-up approach for segmentation assigns each pixel to a cluster plane based on color intensity. A CRF based discriminative model is learned to extract the local neighborhood information in different cluster/color planes. The final category assignment is done by a top-level CRF based on the semantic correlation learned across clusters. The proposed framework has been extensively tested on multi-colored document images with text overlapping graphics/image.