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Survey and taxonomy of information-centric vehicular networking security attacks

Information Centric Networks (ICNs) overcome the current IP-based networks weakness and aim to ensure efficient data distribution. The Main ICN features are location-independent naming, in-network caching, name-based routing, built-in security, and high mobility. ICN vehicular networks stratify the ICN architecture on the Vehicular Ad hoc Networks (VANETs) to reinforce a massive amount of data

Artificial Intelligence
Software and Communications
Innovation, Entrepreneurship and Competitiveness

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on

Artificial Intelligence

A HYBRID RECOMMENDER FRAMEWORK FOR SELECTING A COURSE REFERENCE BOOKS

Recommender systems are receiving great attention these days, as various researchers and major companies are conducting continuous research in this field. Companies like Google and Amazon have provided different effective models for video recommendation systems, but the educational field is poorly studied as other researchers explained. Different researchers proposed various approaches showing the

Artificial Intelligence

Mobility-Aware Edge Caching for Minimizing Latency in Vehicular Networks

This work proposes novel proactive caching schemes for minimizing the communication latency in Vehicular Ad Hoc Networks (VANETs) under freeway and city mobility models. The main philosophy that underlies these schemes is to exploit information that may be available a priori for vehicles' demands and mobility patterns. We consider two paradigms: cooperative, wherein multiple Roadside Units (RSUs)

Artificial Intelligence

QIRHSI: novel quantum image representation based on HSI color space model

We present QIRHSI, a novel quantum image representation method based on the HSI color space model. QIRHSI integrates the advantages of the Flexible Representation of Quantum Images (FRQI) model and the Novel Enhanced Quantum Representation (NEQR) model. On the one hand, the proposed QIRHSI model is better suited for the image processing related to intensity information via binary qubit sequence

Artificial Intelligence

Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

Recently, the numerical optimization field has attracted the research community to propose and develop various metaheuristic optimization algorithms. This paper presents a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA). The proposed algorithm is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for

Circuit Theory and Applications

Security and Interoperability Issues with Internet of Things (IoT) in Healthcare Industry: A Survey

Recently, public healthcare systems become one of the most pivotal parts in our daily life. Resulting in an insane increase in Medical data like medical images and patient information. Having huge amount of data requires more computational power for efficient data management. In addition, data security, privacy and trustworthy have to be maintained and guaranteed. Most medical information in the

Artificial Intelligence

License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)

Although the majority of existing License Plate (LP) recognition techniques have significant improvements in accuracy, they are still limited to ideal situations in which training data is correctly annotated with restricted scenarios. Moreover, images or videos are frequently used in monitoring systems that have Low Resolution (LR) quality. In this work, the problem of LP detection in digital

Artificial Intelligence

Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN

The objective of this research is to build a 'Whole Slide Images' classification system using Convolutional Neural Network (CNN). This system is capable of classifying Thyroid tumors into three types: Follicular adenoma, follicular carcinoma, and papillary carcinoma. Furthermore, the cascaded CNN technique is additionally employed to classify the classified follicular carcinoma into four

Artificial Intelligence
Healthcare

Automated detection of shockable ECG signals: A review

Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a

Artificial Intelligence
Healthcare