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  • Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks
  • Abstract For efficient and robust information exchange in the vehicular ad-hoc network, a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel. In addition, we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards. Unfortunately, there may be some misbehaving nodes and due to their selfish and greedy approach, these nodes may not help others on the network. To deal with this challenge, trust-based misbehavior avoidance schemes are generally…
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  • Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network
  • Abstract Routing is a key function in Wireless Sensor Networks (WSNs) since it facilitates data transfer to base stations. Routing attacks have the potential to destroy and degrade the functionality of WSNs. A trustworthy routing system is essential for routing security and WSN efficiency. Numerous methods have been implemented to build trust between routing nodes, including the use of cryptographic methods and centralized routing. Nonetheless, the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities. At the moment, there is no effective way to avoid malicious node attacks. As a consequence…
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  • IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles
  • Abstract Recent trends in communication technologies and unmanned aerial vehicles (UAVs) find its application in several areas such as healthcare, surveillance, transportation, etc. Besides, the integration of Internet of things (IoT) with cloud computing environment offers several benefits for the UAV communication. At the same time, aerial scene classification is one of the major research areas in UAV-enabled MEC systems. In UAV aerial imagery, efficient image representation is crucial for the purpose of scene classification. The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results. Therefore, the current research work…
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  • Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture
  • Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple…
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  • Using Capsule Networks for Android Malware Detection Through Orientation-Based Features
  • Abstract Mobile phones are an essential part of modern life. The two popular mobile phone platforms, Android and iPhone Operating System (iOS), have an immense impact on the lives of millions of people. Among these two, Android currently boasts more than 84% market share. Thus, any personal data put on it are at great risk if not properly protected. On the other hand, more than a million pieces of malware have been reported on Android in just 2021 till date. Detecting and mitigating all this malware is extremely difficult for any set of human experts. Due to this reason, machine learning–and…
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  • Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks
  • Abstract E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter…
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  • Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks
  • Abstract Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article…
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  • Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis
  • Abstract Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear; with tremendously-limiting measures…
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  • Graph Transformer for Communities Detection in Social Networks
  • Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has…
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  • Deep Neural Network Driven Automated Underwater Object Detection
  • Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an…
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