Automatic Identity Recognition Using Speech Biometric

Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recognizer using speech biometric technology based on Artificial Intelligence (AI) techniques provided in MATLAB. For our phase one, speech data is collected from 20 (10 male and 10 female) participants in order to develop the recognizer. The speech data include utterances recorded for the English language digits (0 to 9), where each participant recorded each digit 3 times, which resulted in a total of 600 utterances for all participants. For our phase two, speech data is collected from 100 (50 male and 50 female) participants in order to develop the recognizer. The speech data is divided into text-dependent and text-independent data, whereby each participant selected his/her full name and recorded it 30 times, which makes up the text-independent data. On the other hand, the text-dependent data is represented by a short Arabic language story that contains 16 sentences, whereby every sentence was recorded by every participant 5 times. As a result, this new corpus contains 3000 (30 utterances * 100 speakers) sound files that represent the text-independent data using their full names and 8000 (16 sentences * 5 utterances * 100 speakers) sound files that represent the text-dependent data using the short story. For the purpose of our phase one of developing the automatic identity recognizer using speech, the 600 utterances have undergone the feature extraction and feature classification phases. The speech-based automatic identity recognition system is based on the most dominating feature extraction technique, which is known as the Mel-Frequency Cepstral Coefficient (MFCC). For feature classification phase, the system is based on the Vector Quantization (VQ) algorithm. Based on our experimental results, the highest accuracy achieved is 76%. The experimental results have shown acceptable performance, but can be improved further in our phase two using larger speech data size and better performance classification techniques such as the Hidden Markov Model (HMM)

Plasma Amino Acids Metabolomics' Important in Glucose Management in Type 2 Diabetes

The perturbation in plasma free amino acid metabolome has been observed previously in diabetes mellitus, and is associated with insulin resistance as well as the onset of cardiovascular disease in this population. In this study, we investigated, for the first time, changes in the amino acid profile in a group of people with and without type 2 diabetes (T2D) with normal BMI, from Jordan, who were only managed on metformin. Twenty one amino acids were evaluated in plasma samples from 124 people with T2D and 67 healthy controls, matched for age, gender and BMI, using amino acids analyser. Total amino acids, essential amino acids, non-essential amino acids and semi-essential amino acids were similar in T2D compared to healthy controls. Plasma concentrations of four essential amino acids were increased in the presence of T2D (Leucine, p < 0.01, Lysine, p < 0.001, Phenylalanine, p < 0.01, Tryptophan, p < 0.05). On the other hand, in relation to non-essential amino acids, Alanine and Serine were reduced in T2D (p < 0.01, p < 0.001, respectively), whereas Aspartate and Glutamate were increased in T2D compared to healthy controls (p < 0.001, p < 0.01, respectively). A semi-essential amino acid, Cystine, was also increased in T2D compared to healthy controls (p < 0.01). Citrulline, a metabolic indicator amino acid, demonstrated lower plasma concentration in T2D compared to healthy controls (p < 0.01). These amino acids were also correlated with fasting blood glucose and HbA1c (p < 0.05). Glutamate, glycine and arginine were correlated with the duration of metformin treatment (p < 0.05). No amino acid was correlated with lipid profiles. Disturbances in the metabolism of these amino acids are closely implicated in the pathogenesis of T2D and associated cardiovascular disease. Therefore, these perturbed amino acids could be explored as therapeutic targets to improve T2D management and prevent associated cardiovascular complications

The Role of Media in Educational Social Construction of Children with Special Needs

This study tries to explore the function that the media plays in supporting the social construction process within the context of inclusive education services. The impact of the media is one of the multiple variables that contribute to the broad adoption of inclusive education. Other contributing aspects include: To be more specific, what part do different kinds of media play in the social construction that makes up inclusive education? It is vital to do qualitative research to shed light on the function that the media plays in building and inviting classrooms to create. The selection of the test population to evaluate the performance of the inclusive education program required a great deal of attention to detail and consideration. The data for this research was gathered using a variety of methods, including observation, interviews, and written records. When looking at the data that was acquired, a descriptive qualitative analysis was performed. It has been found, after considerable discussion, that the speed with which social construction may occur in the classroom is closely tied to the efficacy of the media in aiding student comprehension. This conclusion was reached after much deliberation

Legislative and judicial means against emerging crimes: The crime of money laundering as a model

This study was designed to clarify the effective legislative and judicial means to face the crime of money laundering. It is considered a new and emerging crime, according to an integrated vision that took into account the developments of the era and its variables in order to achieve security in society. The problem of money-laundering has become a phenomenon that alarms states and individuals because of its serious effects in all aspects of political, economic and social life, as well as in its sources of support, as it is fueled by crime in all its forms. The crime of money-laundering has assumed an advanced rank for crime patterns in light of the development of information technologies and means of communication. Therefore, states have sought to establish laws and resolute procedures to prevent the spread of this phenomenon. The judiciary has a duty to bring an end to a number of crimes. This is to be achieved through facing this phenomenon in legislative and judicial ways, using preventive methods, and through the enactment of legislation with severe penalties and judicial application. Preventive measures must also be taken at administrative and financial levels to prevent this crime and dry up its sources. This study is divided into three main topics: the first includes the definition of emerging crimes and the importance of legislatio

Investigating rainfall estimation from radar measurements using neural networks

Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF) neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar) ground radar (KMLB) over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA) technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting

The impact of empowering internal auditors on the quality of electronic internal audits: A case of Jordanian listed services companies

This study emphases on the top management empowerments to internal auditors, namely, general qualification, electronic qualification and independence, affecting quality of electronic internal audits in the Jordanian Listed Service Companies. This paper used 144 usable questionnaires from internal auditors in the Jordanian listed service companies. The gathered data were analysed utilizing ``Statistical Package for Social Sciences (SPSS)''. The results reveal that general qualification, electronic qualification and independence have a significant effect on the quality of electronic internal audits, as supported by the resource-based view. Due to the importance of the service companies’ sector in the context of Jordan, the results are helpful for the internal audit profession and decision makers in offering new legislation for the internal audit profession. Future research may consider other factors that may hinder the quality of electronic internal audits, such as audit task complexity or organizational culture

Vascular endothelial growth factor receptor inhibition enhances chronic obstructive pulmonary disease picture in mice exposed to waterpipe smoke

Background: Chronic obstructive pulmonary disease (COPD) is marked by destruction of alveolar architecture. Preclinical modelling for COPD is challenging. Chronic cigarette smoke exposure, the reference animal model of COPD, is time-inefficient, while exposure to waterpipe smoke (WPS), a surging smoking modality, was not fully tested for its histopathological pulmonary consequences. Since alveolar damage and pulmonary vascular endothelial dysfunction are integral to COPD pathology, lung histopathological effects of WPS were temporally evaluated, alone or in combination with vascular endothelial growth factor receptor (VEGFR) inhibition in mice.Materials and methods: Mice were exposed to WPS, 3 hours/day, 5 days/week, for 1, 2, 3, or 4 months. Another group of mice was exposed to WPS for 1 month, while being subjected to injections with the VEGFR blocker Sugen5416 (SU, 20 mg/kg) 3 times weekly. Control mice were exposed to fresh air in a matching inhalation chamber. Histopathological assessment of COPD was performed. Alveolar destructive index (DI) was counted as the percentage of abnormally enlarged alveoli with damaged septa per all alveoli counted. Mean linear intercept (MLI) was calculated as a measure of airspace enlargement.Results: Exposure to WPS resulted in significant increases in alveolar DI and MLI only after 4 months. Lung inflammatory score was minimal across all time-points. Importantly, combination of WPS and SU resulted in significantly increased DI, MLI, and inflammatory scores as early as 1 month post exposure.Conclusions: Combined exposure to WPS and SU results in COPD picture, highlighting the role of pulmonary vascular endothelial dysfunction in the disease

Automatic identity recognition systems : a review

Rapidly changed computer technology and fast growth of communication ways, makes everyday work easy and managed. Technology takes place everywhere, in business, education, market, security... etc. However, communication between human and these technologies become the main concern of many research areas, especially for developing automatic identity recognition systems. However, biometric technologies are among the most important technologies used in this area. Biometric technology refers to the automatic identity recognition using physical or behavioral traits associated with him/her. Using biometrics, it is possible to establish physiological-based systems that depend on physiological characteristics such as fingerprint, face recognition, DNA... etc, or behavioral-based systems that depend on behavioral characteristics such as gait, voice ...etc, or even combining both of them in one system. Therefore, biometrics technologies can be excellent candidates for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. In addition, biometric technologies are flexible enough to be combined with other tools to produce more secure and easier to use verification system

Arabic automatic continuous speech recognition systems

MSA is the current formal linguistic standard of Arabic language, which is widely taught in schools and universities, and often used in the office and the media. MSA is also considered as the only acceptable form of Arabic language for all native speakers [I]. As recently, the research community has witnessed an improvement in the performance of ASR systems, there is an increasingly widespread use of this technology for several languages of the world. Similarly, research interests have grown significantly in the past few years for Arabic ASR research. It is noticed that Arabic ASR research is not only conducted and investigated by researchers in the Arab world, but also by many others located in different parts of the \vorld especially the western countries

Genetic dissection of grain architecture-related traits in a winter wheat population

Background: The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated. Results: Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767–602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture. Conclusions: These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.Fil: Schierenbeck, Matías. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Cátedra de Cerealicultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Leibniz Institute of Plant Genetics and Crop Plant Research; AlemaniaFil: Alqudah, Ahmad M.. University Aarhus; DinamarcaFil: Lohwasser, Ulrike. Leibniz Institute of Plant Genetics and Crop Plant Research; AlemaniaFil: Tarawneh, Rasha A.. Leibniz Institute of Plant Genetics and Crop Plant Research; AlemaniaFil: Simon, Maria Rosa. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Cátedra de Cerealicultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Börner, Andreas. Leibniz Institute of Plant Genetics and Crop Plant Research; Alemani