Node-Replication Attack Detection in Vehicular Ad-hoc Networks based on Automatic Approach

Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as low as possible. In this type networks, traditional detection algorithms of node replication attacks are not efficient. In this paper, we propose an initial idea to apply a newly adapted statistical methodology that can detect node replication attacks with high performance as compared to state-of-the-art techniques. We provide a sufficient description of this methodology and a road-map for testing and experiment its performance

Automatic extraction of ontological relations from Arabic text

Automatic extraction of semantic relationships among Arabic concepts to formulate ontology models is crucial for providing rich semantic metadata. Due to the annual increase of Arabic content on the Internet, the need for specialized tools to analyze and understand Arabic text has emerged. This research proposes a methodology that extracts ontological relationships. The goals of this research are: to extract semantic features of Arabic text, propose syntactic patterns of relationships among concepts, and propose a formal model of extracting ontological relations. The proposed methodology has been designed to analyze Arabic text using lexical semantic patterns of the Arabic language according to a set of features. Next, the features have been abstracted and enriched with formal descriptions for the purpose of generalizing the resulted rules. The rules, then, have formulated a classifier that accepts Arabic text, analyzes it, and then displays related concepts labeled with its designated relationship. Moreover, to resolve the ambiguity of homonyms, a set of machine translation, text mining, and part of speech tagging algorithms have been reused. We performed extensive experiments to measure the effectiveness of our proposed tools. The results indicate that our proposed methodology is promising for automating the process of extracting ontological relations

ROLEX-SP: Rules of lexical syntactic patterns for free text categorization

Due to the rapid growth of free text documents available in digital form, efficient techniques of automatic categorization are of great importance. In this paper, we present an efficient rule-based method for categorizing free text documents. The contributions of this research are the formation of lexical syntactic patterns as basic classification features, a categorization framework that addresses the problem of classifying free text with minimal label description, and an efficient learning algorithm in terms of time complexity and F-measure. The framework of ROLEX-SP concentrates on capturing the correct classes of text as well as reducing classification errors

Influence of Selected Dietary Plant Extracts on Productive, Physiological, and Viral Immunological Response of Broilers

This experiment was implemented to evaluate the influence of 3 plant extracts involving garlic (GC), cinnamon (CN), and black cumin (BC) powders in broiler chicken diet from 1-42 d on productive, physiological, and immunological traits. In total, 240 birds were assigned into 4 groups, each with 3 replicates. In the control group (CO), the chickens were fed with a balanced diet. Experimental groups were composed by supplementing the diet with 4 mg/kg of diet for each GC, CN, and BC. At 3 and 6 weeks, GC, CN, and BC groups achieved higher body weights, weight gains (p≤0.01), and low feed conversion ratio. GC group recorded low feed intake (p≤0.05) compared to the CO and the other groups from 1 day–6 weeks. GC, CN, and BC groups registered high (p≤0.01) PCV value and lower cholesterol and triglycerides concentrations in serum compared to the CO group. Reduction and increase (p≤0.01) in serum glucose and protein for GC and CN, and CN and BC, respectively, were recorded. High levels of triiodothyronine (T3) (p≤0.05) and thyroid-stimulating hormone (TSH) in GC and CN groups and all treated groups had high concentrations of thyroxine (T4) (p≤0.01) compared to the CO group. Moreover, a clear augmentation in serum antibody titer against Newcastle and Gumboro diseases in GC, CN, and BC compared with the CO group was observed. It was concluded that GC, CN, and BC extracts at the present level may be used to enhance the productive, physiological, and viral immunological characteristics of birds

A Novel Feature-Selection Method for Human Activity Recognition in Videos

Human Activity Recognition (HAR) is the process of identifying human actions in a specific environment. Recognizing human activities from video streams is a challenging task due to problems such as background noise, partial occlusion, changes in scale, orientation, lighting, and the unstable capturing process. Such multi-dimensional and none-linear process increases the complexity, making traditional solutions inefficient in terms of several performance indicators such as accuracy, time, and memory. This paper proposes a technique to select a set of representative features that can accurately recognize human activities from video streams, while minimizing the recognition time and memory. The extracted features are projected on a canvas, which keeps the synchronization property of the spatiotemporal information. The proposed technique is developed to select the features that refer only to progression of changes. The original RGB frames are preprocessed using background subtraction to extract the subject. Then the activity pattern is extracted through the proposed Growth method. Three experiments were conducted; the first experiment was a baseline to compare the classification task using the original RGB features. The second experiment relied on classifying activities using the proposed feature-selection method. Finally, the third experiment provided a sensitivity analysis that compares between the effect of both techniques on time and memory resources. The results indicated that the proposed method outperformed original RBG feature-selection method in terms of accuracy, time, and memory requirements

Measuring Perceived Voice Disorders and Quality of Life among Female University Teaching Faculty

Background: Occupations that require heavy vocal use can place the person at risk of voice disorders (VDs). Heavy demands on the voice, especially for a long time or with loud back-ground noise, can lead to vocal abuse or misuse. The study aimed to measure the prevalence of perceived voice disorders among the teaching faculty at a female university, identify the risk fac-tors that affect their voice, and determine the effect of perceived voice disorders on their quality of life (QoL). Methods: The study sample consisted of female teaching faculty (N = 401). The ques-tionnaire included general sociodemographic data, general voice data, the vocal tract discomfort (VTD) scale, and the World Health Organization Quality of Life assessment (WHOQOL)-BREF. Results: The results demonstrated that 44.1% of the participants had perceived voice disorders, and stress, reflux, and asthma had a significant relationship with self-perceived voice disorders. Furthermore, the data showed that self-perceived voice disorders negatively impacted the overall QoL of teaching faculty. Conclusions: Perceived voice disorders are affected by various factors, including health conditions, medications, and lifestyle choices. Although teaching characteristics and demo-graphic factors are believed to be the cause, in this study they did not significantly contribute to perceived voice disorders. Faculty members with perceived voice disorders have a poorer quality of life, highlighting the need for education on preventative vocal measures and awareness of voice care