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A new static-based framework for ransomware detection

Recently, ransomware attacks are on the rise hitting critical infrastructures and organizations globally. Ransomware uses advanced encryption techniques to encrypt important files on the targeted computer, then it requests payment to decrypt the encrypted files again. Therefore, the detection and prevention of ransomware attacks represent major challenges for security researchers. This research proposes a novel static-based rules ransomware detection framework. The decision rules of the proposed framework are based on static features extracted from the ransomware files. When scanned file

Artificial Intelligence

General Trans-Compiler based Mobile Applications Converter

Deployment on different platforms has been a great issue for mobile companies that aim to maximize the return on investments by making their mobile applications available on different mobile platforms. Consequently, the app may be developed several times to match different platforms. Therefore, there is a need to have solutions that enable the developers to develop the app once, and run it everywhere to reduce the cost of development and reach out to maximum users across several platforms. In this paper a tool is provided with the most popular languages /frameworks (native, cross platform)

Artificial Intelligence

Securing Hardware from Malicious Attacks

Hardware security is considered a major design and manufacturing target area with a broad range of research and development topics such as protection of intellectual property (IP), metering of hardware, detection of hardware Trojans, and a lot of other topics. This paper discusses Trojan realization in integrated circuits (ICs), as well as the possible security measures, also exploring the usage of the 3-D integration in hardware security where additional hardware can be mounted after fabrication to foster secure execution just for those systems which need it. © 2021 IEEE.

Artificial Intelligence

The H3ABioNet helpdesk: An online bioinformatics resource, enhancing Africa's capacity for genomics research

Background: Currently, formal mechanisms for bioinformatics support are limited. The H3Africa Bioinformatics Network has implemented a public and freely available Helpdesk (HD), which provides generic bioinformatics support to researchers through an online ticketing platform. The following article reports on the H3ABioNet HD (H3A-HD)'s development, outlining its design, management, usage and evaluation framework, as well as the lessons learned through implementation. Results: The H3A-HD evaluated using automatically generated usage logs, user feedback and qualitative ticket evaluation

Artificial Intelligence

Utilization of Machine Learning In RTL-GL Signals Correlation

Verification is an important part of the Electronic Design Automation (EDA) design flow which currently takes a considerable amount of time. During the synthesis process, Different optimizations are done to the Register-Transfer-Level (RTL) code to optimize the power, area, and speed of the circuit. These optimizations result in changes in the names of signals at the gate level. Automatic signal mapping can improve the verification process and can be used to guide functional verification activities between the two presentations using (Clock domain crossing (CDC) analysis in RTL, Gate Level CDC

Artificial Intelligence

Using machine learning algorithms for breast cancer diagnosis

There are many cancer patients, especially breast cancer patients as it is the most common type of cancer. Due to the huge number of breast cancer patients, many breast cancer-focused hospitals aren't able to process the huge number of patients and might expose some women to late stages of cancer. Thus, the automation of the process can help these hospitals in speeding up the process of cancer detection. In this paper, the authors test several machine learning models such as k-nearest neighbours (KNN), support vector machine (SVM), and artificial neural network (ANN). They then compare their

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 than multi-channel representation for quantum image (MCQI) (multi-channel FRQI). On the other hand, the QIRHSI model requires less storage space (10 qubits) in the hue and saturation channels, compared

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) collaborate to expedite the transfer of information to the intended user, and non-cooperative, wherein each RSU operates independently of other RSUs in the network. To develop the proposed schemes

Artificial Intelligence

The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients'

Artificial Intelligence

Managing Delivery of Safeguarding Substances as a Mitigation against Outbreaks of Pandemics

The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics. A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period. A nonlinear binary mathematical programming model for the problem is formulated. The decision variables are binary ones that represent whether to choose a specic consumer, and design constraints are formulated to keep track of the

Artificial Intelligence