A New Model of M-secure Image Via Quantization

Bhandari, Vijay; Tamrakar, Sitendra; Shukla, Piyush Kumar; Bhandari, Arpana

doi:10.1007/978-981-13-6351-1_26

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…According to Yang and colleagues (2010) [ 43 ], an approach that is more efficient than the previous one may be achieved by combining conventional algorithms with bandwidth adaptive algorithms. Identifying and tracking the target is accomplished by the use of background removal in combination with the mean shift target tracking technique [ 44 ]. A model update strategy based on an object kernel histogram was proposed, with the object kernel histogram serving as the basis.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

Jayasudha

Dadheech

Prasad³

et al. 2022

Journal of Healthcare Engineering

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The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.

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Section: Literature Surveymentioning

confidence: 99%

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

Jayasudha

Dadheech

Prasad³

et al. 2022

Journal of Healthcare Engineering

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“…Other important approaches found in the literature include ND for mobile WSN [ 27 , 36 ], RSSI for distance measure [ 28 ], self-adaptive ND [ 37 ], proactive ND for mobiles [ 38 ], ND with slot length control [ 39 ], ND with multipacket reception model [ 30 ], ND model known as Panacea [ 40 ], prime number-based ND [ 41 ], and group-based ND [ 32 ]. Secure critical data reclamation methods for isolated clusters in WSN were proposed in [ 33 , 42 ].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Block-Based Lightweight Machine Learning-Based Predictive Models for Quality Preserving in the Internet of Things- (IoT-) Based Medical Images with Diagnostic Applications

Reshma

Khan

Niranjanamurthy

et al. 2022

Computational Intelligence and Neuroscience

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In the contemporary era of unprecedented innovations such as the Internet of Things (IoT), modern applications cannot be imagined without the presence of a wireless sensor network (WSN). Nodes in WSN use neighbor discovery (ND) protocols to have necessary communication among the nodes. The neighbor discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximum percentage of neighbors discovered. The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots. This work develops a lightweight intrusion detection system (IDS) based on two machine learning approaches, namely, feature selection and feature classification, in order to improve the security of the Internet of Things (IoT) while transferring medical data through a cloud platform. In order to take advantage of the comparatively cheap processing cost of the filter-based technique, the feature selection was carried out. The two methods are found to have certain drawbacks. The first category disturbs the original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbors. When the second category is followed, it may have inefficient slots in the wake-up schedules leading to performance degradation. Therefore, the motivation behind the work in this paper is that by combining the two categories, it is possible to reap the benefits of both and get rid of the limitations of both. Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring the original integrity of wake-up schedules and adding efficient active slots. Thus, a Hybrid Approach to Neighbor Discovery (HAND) protocol is realized in WSN. The simulation study revealed that HAND outperforms the existing indirect ND models.

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“…According to Figure 8(b) , the complete remaining potency of the system per unit of time stays constant up to and including the death of the first node [ 33 ], regardless of the state of the other nodes in the network [ 34 , 35 ]. As a consequence, the procedure is performed over and over again until the simulation period has been reached.…”

Section: Proposed Energy Protocol With Blockchainmentioning

confidence: 99%

Smart Healthcare System with Light-Weighted Blockchain System and Deep Learning Techniques

Singh

Mir

et al. 2022

Computational Intelligence and Neuroscience

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A radio communication sensor system is a collection of sensor modules that are connected to one another through wireless communication. It is common for them to be battery-powered and responsive to a nearby controller, referred to as the base station. They are capable of doing basic computations and transferring information to the base station in most scenarios. They are also in charge of transporting data from distant nodes, putting a burden on nodes with limited resources, and contributing to the quick depletion of energy in these nodes in the process. Nodes in close proximity to the base station are responsible for more than only detecting and sending data to the base station; they are also responsible for transmitting data from faraway nodes. To reward nodes that perform well, a protocol known as the Improved Fuzzy Inspired Energy Effective Protocol (IFIEEP) employs three separate sorts of nodes in order to provide more energy to those who do not. It takes into account the remaining node energy, the node's proximity to the base station, the node's neighbor concentration, and the node's centrality in a cluster when determining node viability. All of these assumptions are founded on a shaky understanding of the situation. Adaptive clustering must be applied to the most viable nodes in order to identify cluster leaders and transmit data to the base station, in addition to disseminating data across the rest of the network, in order to achieve success. In addition, the research provides proper heterogeneity parameters, which describe, among other things, the number of nodes as well as the starting energy of each node. The percentage gain in-network lifetime when compared to current approaches is minor for smaller numbers of supernodes; however, the percentage gain in the area covered 12.89 percent and 100% when more significant numbers of super nodes are used. These improvements in stability, residual energy, and throughput are accomplished by combining these improvements while also taking into consideration the previously neglected energy-intensive sensing energy aspect. The protocol that has been presented is meant to be used in conjunction with applications that make use of blockchain technology.

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A New Model of M-secure Image Via Quantization

Cited by 8 publications

References 9 publications

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

Hybrid Block-Based Lightweight Machine Learning-Based Predictive Models for Quality Preserving in the Internet of Things- (IoT-) Based Medical Images with Diagnostic Applications

Smart Healthcare System with Light-Weighted Blockchain System and Deep Learning Techniques

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