Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the limitation of traditional AVs, we proposed a virus detection system based on extracting Application Program Interface (API) calls from virus behaviors. The proposed research uses static analysis of behavior-based detection mechanism without executing of software to detect viruses at user mod by using Markov Chain.
In this paper, we present multiple bit error correction coding scheme based on extended Hamming product code combined with type II HARQ using shared resources for on chip interconnect. The shared resources reduce the hardware complexity of the encoder and decoder compared to the existing three stages iterative decoding method for on chip interconnects. The proposed method of decoding achieves 20% and 28% reduction in area and power consumption respectively, with only small increase in decoder delay compared to the existing three stage iterative decoding scheme for multiple bit error correction. The proposed code also achieves excellent improvement in residual flit error rate and up to 58% of total power consumption compared to the other err
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A new optoelectronic flow injection method is proposed for the determination of ferrous ions (Fe 2+ ) based on thiocyanate complexation to form a deep-red FeSCN 2+ complex.
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Many carbonate reservoirs in the world show a tilted in originally oil-water contact (OOWC) which requires a special consideration in the selection of the capillary pressure curves and an understanding of reservoir fluids distribution while initializing the reservoir simulation models.
An analytical model for predicting the capillary pressure across the interface that separates two immiscible fluids was derived from reservoir pressure transient analysis. The model reflected the entire interaction between the reservoir-aquifer fluids and rock properties measured under downhole reservoir conditions.
This model retained the natural coupling of oil reservoirs with the aquifer zone and treated them as an explicit-region composite system
The use of credit cards for online purchases has significantly increased in recent years, but it has also led to an increase in fraudulent activities that cost businesses and consumers billions of dollars annually. Detecting fraudulent transactions is crucial for protecting customers and maintaining the financial system's integrity. However, the number of fraudulent transactions is less than legitimate transactions, which can result in a data imbalance that affects classification performance and bias in the model evaluation results. This paper focuses on processing imbalanced data by proposing a new weighted oversampling method, wADASMO, to generate minor-class data (i.e., fraudulent transactions). The proposed method is based on th
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Geotechnical engineering like any other engineering field has to develop and cope with new technologies. This article intends to investigate the spatial relationships between soil’s liquid limit (LL), plasticity index (PI) and Liquidity index (LI) for particular zones of Sulaymaniyah City. The main objective is to study the ability to produce digital soil maps for the study area and determine regions of high expansive soil. Inverse Distance Weighting (IDW) interpolation tool within the GIS (Geographic Information System) program was used to produce the maps. Data from 592 boreholes for LL and PI and 245 boreholes for LI were used for this study. Layers were allocated into three depth ranges (1 to 2, 2 to 4 and 4 to 6)
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The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med
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The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di
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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural
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Most vegetation’s are Land cover (LC) for the globe, and there is an increased attention to plants since they represent an element of balance to natural ecology and maintain the natural balance of rapid changes due to systematic and random human uses, including the subject of the current study (Bassia eriophora ) Which represent an essential part of the United Nations system for land cover classification (LCCS), developed by the World Food Organization (FAO) and the world Organization for environmental program (UNEP), to observe basic environmental elements with modern techniques. Although this plant is distributed all over Iraq, we found that this plant exists primarily in the middle
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