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  • Image Steganography Using Deep Neural Networks
  • Abstract Steganography is the technique of hiding secret data within ordinary data by modifying pixel values which appear normal to a casual observer. Steganography which is similar to cryptography helps in secret communication. The cryptography method focuses on the authenticity and integrity of the messages by hiding the contents of the messages. Sometimes, it is not only just enough to encrypt the message but also essential to hide the existence of the message itself. As this avoids misuse of data, this kind of encryption is less suspicious and does not catch attention. To achieve this, Stacked Autoencoder model is developed which…
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  • Sport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network
  • Abstract Numerous learning-based techniques for effective human activity recognition (HAR) have recently been developed. Wearable inertial sensors are critical for HAR studies to characterize sport-related activities. Smart wearables are now ubiquitous and can benefit people of all ages. HAR investigations typically involve sensor-based evaluation. Sport-related activities are unpredictable and have historically been classified as complex, with conventional machine learning (ML) algorithms applied to resolve HAR issues. The efficiency of machine learning techniques in categorizing data is limited by the human-crafted feature extraction procedure. A deep learning model named MBiGRU (multimodal bidirectional gated recurrent unit) neural network was proposed to recognize everyday…
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  • Deep Learning Based Residual Network Features for Telugu Printed Character Recognition
  • Abstract In India, Telugu is one of the official languages and it is a native language in the Andhra Pradesh and Telangana states. Although research on Telugu optical character recognition (OCR) began in the early 1970s, it is still necessary to develop effective printed character recognition for the Telugu language. OCR is a technique that aids machines in identifying text. The main intention in the classifier design of the OCR systems is supervised learning where the training process takes place on the labeled dataset with numerous characters. The existing OCR makes use of patterns and correlations to differentiate words from other…
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  • Smart Greenhouse Control via NB-IoT
  • Abstract The Internet of Things (IoT) has flourished in recent years, which brings convenience to people’s lives, improves the quality of life, allows more effectively managing and maximizing benefits in industry, and improves weather predictions as the impact of global warming has complicated traditional methods to infer the weather. To this end, agriculture has also given more attention to greenhouse cultivation. In the early days of industrial research, Wi-Fi and ZigBee were used as short-or medium-distance communication technologies for transmissions in the network layer of the IoT architecture. Instead of long-distance communication technologies, such as LoRa and NB-IoT, the features of…
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  • Multi-Objective Immune Algorithm for Internet of Vehicles for Data Offloading
  • Abstract On the Internet of Vehicle (IoV) devices, offloading data is the major problem because massive amounts of data generate energy consumption, and the execution cost is high. At present, accidents traffic management is highly prominent due to increased vehicles among the population. IoV is the only technology to help the transport system effectively. This data outreach the memory also has high energy consumption, and the storage cost is high. To overcome these issues, a Mobility aware Offloading scheme with Multi-Objective Immune Optimization algorithm (MOS-MOIO) is used in the cloud storage. The data is generated from the online sensor system. The…
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  • Adaptive Multicale Transformation Run-Length Code-Based Test Data Compression in Benchmark Circuits
  • Abstract Test data volume reduction and power consumption during testing time outlines are two main problems for Very Large Scale Integration (VLSI) gadgets. Most the code-based arrangements have been utilized to diminish test data volume, although the most notable way that test data volume is high. The switching action that happens between the test carriers leads would expand power consumption. This work presents a compression/decompression methodology for limiting the amount of test data that should be kept on a tester and conveyed to each center in a System on a Chip (SOC) during a test utilizing the Adaptive Multiscale Transformation Run…
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  • A Novel Anomaly Detection Method in Sensor Based Cyber-Physical Systems
  • Abstract In recent times, Cyber-physical system (CPS) integrates the cyber systems and physical world for performing critical processes that are started from the development in digital electronics. The sensors deployed in CPS are commonly employed for monitoring and controlling processes that are susceptible to anomalies. For identifying and detecting anomalies, an effective anomaly detection system (ADS) is developed. But ADS faces high false alarms and miss detection rate, which led to the degraded performance in CPS applications. This study develops a novel deep learning (DL) approach for anomaly detection in sensor-based CPS using Bidirectional Long Short Term Memory with Red Deer…
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  • Security Protocol Function Using Quantum Elliptic Curve Cryptography Algorithm
  • Abstract Quantum Computing (QC). The content of node or sink nodes is processed using the fundamental principles of quantum mechanics. However, cryptography techniques face several other issues, such as availability, integrity, and vulnerability, to name a few. The researchers have overcome many obstacles, yet security remains a crucial concern in QC. However, experimenters recently discovered that the QC has a lot more data hacking than static networks. Moreover, the bitwise error is still present in implementing the Quantum Computing Cryptography Protocol (QCCP). Because all nodes are mobile and dynamic topology occurs, the proposed research uses the Quantum Elliptical Curve Cryptography (QECC)…
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  • Convolutional Neural Networks Based Video Reconstruction and Computation in Digital Twins
  • Abstract With the advancement of communication and computing technologies, multimedia technologies involving video and image applications have become an important part of the information society and have become inextricably linked to people's daily productivity and lives. Simultaneously, there is a growing interest in super-resolution (SR) video reconstruction techniques. At the moment, the design of digital twins in video computing and video reconstruction is based on a number of difficult issues. Although there are several SR reconstruction techniques available in the literature, most of the works have not considered the spatio-temporal relationship between the video frames. With this motivation in mind, this…
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  • Deep Learning Based Distributed Intrusion Detection in Secure Cyber Physical Systems
  • Abstract Cyber Physical Systems (CPSs) are network systems containing cyber (computation, communication) and physical (sensors, actuators) components that interact with each other through feedback loop with the help of human intervention. The dynamic and disseminated characteristics of CPS environment makes it vulnerable to threats that exist in virtualization process. Due to this, several security issues are presented in CPS. In order to address the challenges, there is a need exists to extend the conventional security solutions such as Intrusion Detection Systems (IDS) to handle high speed network data traffic and adaptive network pattern in cloud. Additionally, the identification of feasible network…
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