Search results for: self-adaptive genetic algorithms

Authors: Ming Liu, Chong Wu, Bingquan Liu, Lei Chen

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Internet users have to face the massive amount of textual data every day. Organizing texts into categories can help users dig the useful information from large-scale text collection. Clustering, in fact, is one of the most promising tools for categorizing texts due to its unsupervised characteristic. Unfortunately, most of traditional clustering algorithms lose their high qualities on large-scale text collection. This situation mainly attributes to the high- dimensional vectors generated from texts. To effectively and efficiently cluster large-scale text collection, this paper proposes a vector reconstruction based clustering algorithm. Only the features that can represent the cluster are preserved in cluster’s representative vector. This algorithm alternately repeats two sub-processes until it converges. One process is partial tuning sub-process, where feature’s weight is fine-tuned by iterative process. To accelerate clustering velocity, an intersection based similarity measurement and its corresponding neuron adjustment function are proposed and implemented in this sub-process. The other process is overall tuning sub-process, where the features are reallocated among different clusters. In this sub-process, the features useless to represent the cluster are removed from cluster’s representative vector. Experimental results on the three text collections (including two small-scale and one large-scale text collections) demonstrate that our algorithm obtains high quality on both small-scale and large-scale text collections.

Keywords: vector reconstruction, large-scale text clustering, partial tuning sub-process, overall tuning sub-process

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Authors: Juliana A. Knocikova

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Methods of nonlinear signal analysis are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Considering the dynamical systems, entropy is usually understood as a rate of information production. Changes in temporal dynamics of physiological data are indicating evolving of system in time, thus a level of new signal pattern generation. During last decades, many algorithms were introduced to assess some patterns of physiological responses to external stimulus. However, the reflex responses are usually characterized by short periods of time. This characteristic represents a great limitation for usual methods of nonlinear analysis. To solve the problems of short recordings, parameter of approximate entropy has been introduced as a measure of system complexity. Low value of this parameter is reflecting regularity and predictability in analyzed time series. On the other side, increasing of this parameter means unpredictability and a random behavior, hence a higher system complexity. Reduced neurophysiological data complexity has been observed repeatedly when analyzing electroneurogram and electromyogram activities during defence reflex responses. Quantitative phrenic neurogram changes are also obvious during severe hypoxia, as well as during airway reflex episodes. Concluding, the approximate entropy parameter serves as a convenient tool for analysis of reflex behavior characterized by short lasting time series.

Keywords: approximate entropy, neurophysiological data, nonlinear dynamics, reflex

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Jaiyeop Lee

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Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals.

Keywords: rapid detection, SARS-CoV-2, sensitive detection, wastewater surveillance

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Authors: Loai AbdAllah, Mahmoud Kaiyal

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Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

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Authors: Anupama Sahoo, Bongyong Lee, Katia Boniface, Julien Seneschal, Sanjaya K. Sahoo, Tatsuya Seki, Chunyan Wang, Soumen Das, Xianlin Han, Michael Steppie, Sudipta Seal, Alain Taieb, Ranjan J. Perera

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Vitiligo is a common, chronic skin disorder characterized by loss of epidermal melanocytes and progressive depigmentation. Vitiligo has a complex immune, genetic, environmental, and biochemical etiology, but the exact molecular mechanisms of vitiligo development and progression, particularly those related to metabolic control, are poorly understood. Here we characterized the human vitiligo cell line PIG3V and the normal human melanocytes, HEM-l by RNA-sequencing, targeted metabolomics, and shotgun lipidomics. Melanocyte-enriched miR-211, a known metabolic switch in non-pigmented melanoma cells, was severely downregulated in vitiligo cell line PIG3V and skin biopsies from vitiligo patients, while its novel predicted targets transcriptional co-activator PGC1-α (PPARGC1A), ribonucleotide reductase regulatory subunit M2 (RRM2), and serine-threonine protein kinase TAO1 (TAOK1) were reciprocally upregulated. miR-211 binds to PGC1-α 3’UTR locus and represses it. Although mitochondrial numbers were constant, mitochondrial complexes I, II, and IV and respiratory responses were defective in vitiligo cells. Nanoparticle-coated miR-211 partially augmented the oxygen consumption rate in PIG3V cells. The lower oxygen consumption rate, changes in lipid and metabolite profiles, and increased reactive oxygen species production observed in vitiligo cells appear to be partly due to abnormal regulation of miR-211 and its target genes. These genes represent potential biomarkers and therapeutic targets in human vitiligo.

Keywords: metabolism, microRNA, mitochondria, vitiligo

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Fhulufhelo Ramukhithi, Kgothatso Masethe, Tlou Chokoe, Ayanda Maqhashu, Julius Sebei, Tshililo Raphulu, Joseph Mugwabana

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Phenotypic characterisation ensures that the physical appearance of an animal is well documented. The information provided by this phenotypic characterisation study is important for planning management and the use of animal genetic resources. The aim of this study was to characterise the phenotypic characteristics of Bapedi sheep. Bapedi sheep are at risk of extinction like most of the indigenous breeds. As a result, a total of 196 Bapedi ewes and 35 rams were used. Phenotypic-qualitative characteristics were evaluated through visual appraisal. Phenotypic-quantitative characteristics such as body parts measurements were obtained using a flexible tape (cm), while body weight were obtained by using a weighing scale (kg). Bapedi rams (97 %) had higher satisfactory body condition when compared to ewes (75 %). A higher proportion of Bapedi sheep that did not have ticks observed (ewes = 87 % and rams = 91 %). Brown and white colour combination (head x body) was dominating in Bapedi sheep (80 % ewes and 91 % rams). Bapedi ewes did not have any horns; however, 3 % of rams had them. Bapedi sheep had a higher proportion of brown eyes, moderate neck, stiff sideways ears and normal front legs. Bapedi rams had a higher proportion of well-balanced and good attached testicles. Bapedi ewes had average (45 %), small (40 %) and big udders (15 %). Bapedi rams had a significantly higher body weight, height, depth, hearth girth circumference, rump width, hind leg width and length compared to ewes. However, both Bapedi rams and ewes had similar age, body condition score, tail length, length below hock and knee. In conclusion, Bapedi sheep had a higher satisfactory body condition and brown and white colour combination. Some of Bapedi rams’ quantitative characteristics were higher compared to ewes.

Keywords: extinction, indigenous, phenotypic, smallstock

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Jennifer M. Glennon, Hana D'Souza, Luke Mason, Annette Karmiloff-Smith, Michael S. C. Thomas

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Genetic syndrome groups that feature high rates of autism comorbidity, like Down syndrome (DS) and fragile X syndrome (FXS), have been presented as useful models for understanding risk and protective factors involved in the emergence of autistic traits. Yet despite reaching clinical thresholds, these ‘syndromic’ forms of autism appear to differ in important ways from the idiopathic or ‘non-syndromic’ autism phenotype. To uncover the true nature of these comorbidities, it is necessary to extend definitions of autism to include the cognitive characteristics of the disorder and to then apply this broadened conceptualisation to the study of syndromic autism profiles. The current study employs a variety of well-established eye-tracking paradigms to assess visual attentional performance in children with DS and FXS who reach thresholds for autism on the Social Communication Questionnaire. It investigates whether autism profiles in these children are accompanied by visual orienting difficulties (‘sticky attention’), decreased social attention, and enhanced visual search performance, all of which are characteristic of the idiopathic autism phenotype. Data is collected from children with DS and FXS aged between 6 and 10 years, in addition to two control groups matched on age and intellectual ability (i.e., children with idiopathic autism and neurotypical controls). Cross-sectional developmental trajectory analyses are conducted to enable visuo-attentional profile comparisons. Significant differences in the visuo-attentional processes underpinning autism presentations in children with FXS and DS are hypothesised, supporting notions of syndrome specificity. The study provides insight into the complex heterogeneity associated with syndromic autism presentations and autism per se, with clinical implications for the utility of autism intervention programmes in DS and FXS populations.

Keywords: autism, down syndrome, fragile X syndrome, eye tracking

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Authors: Riyas Peringattuthodiyil, Mark Taylor, Ian Wallace, Ailish Nugent, Mike Mitchell, Judith Thompson, Allison McKee, Philip C. Johnston

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Aim of Study: The purpose of the study was to screen for diabetes through HbA1c in patients with chronic pancreatitis (CP) within the Belfast Trust. Background: Patients with chronic pancreatitis are at risk of developing diabetes, earlier diagnosis with subsequent multi-disciplinary input has the potential to improve clinical outcomes. Methods: Clinical and laboratory data of patients with chronic pancreatitis were obtained through the Northern Ireland Electronic Healthcare Record (NIECR), specialist hepatobiliary, and gastrointestinal clinics. Patients were invited to have a blood test for HbA1c. Newly diagnosed patients with diabetes were then invited to attend a dedicated Belfast City Hospital (BCH) specialist chronic pancreatitis and diabetes clinic for follow up. Results: A total of 89 chronic pancreatitis patients were identified; Male54; Female:35, mean age 52 years, range 12-90 years. Aetiology of CP included alcohol 52/89 (58%), gallstones 18/89 (20%), idiopathic 10/89 11%, 2 were genetic, 1: post ECRP, 1: IgG autoimmune, 1: medication induced, 1: lipoprotein lipase deficiency 1: mumps, 1: IVDU and 1: pancreatic divisum. No patients had pancreatic carcinoma. Mean duration of CP was nine years, range 3-30 years. 15/89 (16%) of patients underwent previous pancreatic surgery/resections. Recent mean BMI was 25.1 range 14-40 kg/m². 62/89 (70%) patients had HbA1c performed. Mean HbA1c was 42 mmol/mol, range 27-97mmol/mol, 42/62 (68%) had normal HbA1c (< 42 mmol/mol) 13/62 (21%) had pre-diabetes (42-47mmol/mol) and 7/62 (11%) had diabetes (≥ 48 mmol/mol). Conclusions: Of those that participated in the screening program around one-third of patients with CP had glycaemic control in the pre and diabetic range. Potential opportunities for improving screening rates for diabetes in this cohort could include regular yearly testing at gastrointestinal and hepatobiliary clinics.

Keywords: pancreatogenic diabetes, screening, chronic pancreatitis, trust experience

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Authors: Martynova Alina, Lyubov Buzoleva

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Streptococcus pneumoniae is the causative agent of pneumonias and meningitids throught all the world. Having high genetic diversity, this microorganism can cause different clinical forms of pneumococcal infections and microbiologically it is really difficult diagnosed by routine methods. Also, epidemiological surveillance requires more developed methods of molecular typing because the recent method of serotyping doesn't allow to distinguish invasive and non-invasive isolates properly. Non-coding genome of bacteria seems to be the interesting source for seeking of highly distinguishable markers to discriminate the subspecies of such a variable bacteria as Streptococcus pneumoniae. Technically, we proposed scheme of discrimination of S.pneumoniae strains with amplification of non-coding region (SP_1932) with the following restriction with 2 types of enzymes of Alu1 and Mn1. Aim: This research aimed to compare different methods of typing and their application for molecular epidemiology purposes. Methods: we analyzed population of 100 strains of S.pneumoniae isolated from different patients by different molecular epidemiology methods such as pulse-field gel electophoresis (PFGE), restriction polymorphism analysis (RFLP) and multilolocus sequence typing (MLST), and all of them were compared with classic typing method as serotyping. The discriminative power was estimated with Simpson Index (SI). Results: We revealed that the most discriminative typing method is RFLP (SI=0,97, there were distinguished 42 genotypes).PFGE was slightly less discriminative (SI=0,95, we identified 35 genotypes). MLST is still the best reference method (SI=1.0). Classic method of serotyping showed quite weak discriminative power (SI=0,93, 24 genotypes). In addition, sensivity of RFLP was 100%, specificity was 97,09%. Conclusion: the most appropriate method for routine epidemiology surveillance is RFLP with non-coding region of Streptococcsu pneumoniae, then PFGE, though in some cases these results should be obligatory confirmed by MLST.

Keywords: molecular epidemiology typing, non-coding genome, Streptococcus pneumoniae, MLST

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Authors: Selim A., Albasher N., Bakrmom G., Alanzi S.

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Irritable bowel syndrome (IBS) is a chronic functional bowel disorder characterized by abdominal discomfort or pain and associated with alteration in frequency and/or form of bowel habit among other symptoms. This diagnosis is associated with increased levels of psychological distress, maladaptive coping, genetic risk factors, abnormal small and colonic intestine transit, change in stool frequency or form and abdominal discomfort or pain. Aim: The aim of the study was to assess psychological alarm among individuals suffering from Irritable Bowel Syndrome (IBS). Methods: A cross-sectional correlational research design was used to conduct the current study. A convenience sample of 504 participants was included in the present study. Data were collected using a self-report questionnaire. The questionnaire included socio-demographic data, ROME III to identify Irritable Bowel Syndrome (IBS) and Psychological Alarm Questionnaire. Results: Out of 504 participants who reported abdominal discomfort, 297 (58.9 %) participants met the diagnostic criteria of IBS. The mean age of the IBS participants was 30.16 years, females composed 75.1% of the IBS participants, and 55.2% did not seek medical help. Psychological alarms such as feeling anxious, feeling depressed, having suicidal ideations, bodily pain, having impaired functioning due to pain and feeling unable to cope with pain were significantly high among IBS individuals when compared to individuals not suffering from IBS. Psychological alarms such as feeling anxious, feeling depressed, having suicidal ideations, bodily pain, having impaired functioning due to pain and feeling unable to cope with pain were significantly high among IBS individuals compared to individuals not suffering from IBS. Conclusion: IBS is highly associated with significant psychological alarms including depression, anxiety and suicidal ideas.

Keywords: abdominal pain , irritable bowel syndrome, distress, psychological alarms

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Authors: Siddharatha Sharma

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With the advent of technology and machines, the role of biometrics in society is taking an important place for secured living. Security issues are the major concern in today’s world and continue to grow in intensity and complexity. Biometrics based recognition, which involves precise measurement of the characteristics of living beings, is not a new method. Fingerprints are being used for several years by law enforcement and forensic agencies to identify the culprits and apprehend them. Biometrics is based on four basic principles i.e. (i) uniqueness, (ii) accuracy, (iii) permanency and (iv) peculiarity. In today’s world fingerprints are the most popular and unique biometrics method claiming a social benefit in the government sponsored programs. A remarkable example of the same is UIDAI (Unique Identification Authority of India) in India. In case of fingerprint biometrics the matching accuracy is very high. It has been observed empirically that even the identical twins also do not have similar prints. With the passage of time there has been an immense progress in the techniques of sensing computational speed, operating environment and the storage capabilities and it has become more user convenient. Only a small fraction of the population may be unsuitable for automatic identification because of genetic factors, aging, environmental or occupational reasons for example workers who have cuts and bruises on their hands which keep fingerprints changing. Fingerprints are limited to human beings only because of the presence of volar skin with corrugated ridges which are unique to this species. Fingerprint biometrics has proved to be a high level authentication system for identification of the human beings. Though it has limitations, for example it may be inefficient and ineffective if ridges of finger(s) or palm are moist authentication becomes difficult. This paper would focus on uniqueness of fingerprints to the human beings in comparison to other living beings and review the advancement in emerging technologies and their limitations.

Keywords: fingerprinting, biometrics, human beings, authentication

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Authors: Nafiseh Mollaei, Reihaneh Kardehi Moghaddam

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In case of Diabetes Mellitus the controlling of insulin is very difficult. This illness is an incurable disease affecting millions of people worldwide. Glucose is a sugar which provides energy to the cells. Insulin is a hormone which supports the absorption of glucose. Fuzzy control strategy is attractive for glucose control because it mimics the first and second phase responses that the pancreas beta cells use to control glucose. We propose two control algorithms a type-1 fuzzy controller and an interval type-2 fuzzy method for the insulin infusion. The closed loop system has been simulated for different patients with different parameters, in present of the food intake disturbance and it has been shown that the blood glucose concentrations at a normoglycemic level of 110 mg/dl in the reasonable amount of time. This paper deals with type 1 diabetes as a nonlinear model, which has been simulated in MATLAB-SIMULINK environment. The novel model, termed the Augmented Minimal Model is used in the simulations. There are some uncertainties in this model due to factors such as blood glucose, daily meals or sudden stress. In addition to eliminate the effects of uncertainty, different control methods may be utilized. In this article, fuzzy controller performance were assessed in terms of its ability to track a normoglycemic set point (110 mg/dl) in response to a [0-10] g meal disturbance. Finally, the development reported in this paper is supposed to simplify the insulin delivery, so increasing the quality of life of the patient.

Keywords: interval type-2, fuzzy controller, minimal augmented model, uncertainty

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Authors: Jaswinder Kaur, Nitika, Navneet Kaur, Rajesh Khanna

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A hand-fan shaped microstrip patch antenna (MPA) for L-band and S-band applications is designed, and its characteristics have been reconnoitered. The proposed microstrip patch antenna with double U-slot defected ground structure (DGS) is fabricated on an FR4 substrate which is a very readily available and inexpensive material. The suggested antenna is optimized using Orthogonal Design Method (ODM) of Design of Experiments (DOE) to cover the frequency range from 0.91-2.82 GHz for L-band and S-band applications. The L-band covers the frequency range of 1-2 GHz, which is allocated to telemetry, aeronautical, and military systems for passive satellite sensors, weather radars, radio astronomy, and mobile communication. The S-band covers the frequency range of 2-3 GHz, which is used by weather radars, surface ship radars and communication satellites and is also reserved for various wireless applications such as Worldwide Interoperability for Microwave Access (Wi-MAX), super high frequency radio frequency identification (SHF RFID), industrial, scientific and medical bands (ISM), Bluetooth, wireless broadband (Wi-Bro) and wireless local area network (WLAN). The proposed method of optimization is very time efficient and accurate as compared to the conventional evolutionary algorithms due to its statistical strategy. Moreover, the antenna is tested, followed by the comparison of simulated and measured results.

Keywords: design of experiments, hand fan shaped MPA, L-Band, orthogonal design method, S-Band

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Moez Yeddes

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The property of opacity is widely used in the formal verification of security in computer systems and protocols. Opacity is a general language-theoretic scheme of many security properties of systems. Opacity is parametrized with framework in which several security properties of a system can be expressed. A secret behaviour of a system is opaque if a passive attacker can never deduce its occurrence from the system observation. Instead of considering the case of static observability where the set of observable events is fixed off-line or dynamic observability where the set of observable events changes over time depending on the history of the trace, we introduce Orwellian partial observability where unobservable events are not revealed provided that downgrading events never occurs in the future of the trace. Orwellian partial observability is needed to model intransitive information flow. This Orwellian observability is knwon as ipurge function. We show in previous work how to verify opacity for regular secret is opaque for a regular language L w.r.t. an Orwellian projection is PSPACE-complete while it has been proved undecidable even for a regular language L w.r.t. a general Orwellian observation function. In this paper, we address two problems of opacification of a regular secret ϕ for a regular language L w.r.t. an Orwellian projection: Given L and a secret ϕ ∈ L, the first problem consist to compute some minimal regular super-language M of L, if it exists, such that ϕ is opaque for M and the second consists to compute the supremal sub-language M′ of L such that ϕ is opaque for M′. We derive both language-theoretic characterizations and algorithms to solve these two dual problems.

Keywords: security policies, opacity, formal verification, orwellian observation

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Authors: Masoud Soltanialvar, Ali Bagherpour

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Documented cases of human infection with H9N2 avian influenza viruses, first detected in 1999 in Hong Kong and China, indicate that these viruses can be directly transmitted from birds to humans. In this study, we characterized the mutation in the Hemagglutinin (HA) genes and proteins that correlates with a shift in affinity of the Hemagglutinin (HA) protein from the “avian” type sialic receptors to the “human” type in 10 Iranian isolates. We delineated the genomes and receptor binding profile of HA gene of some field isolates and established their phylogenetic relationship to the other Asian H9N2 sub lineages. A total of 1200 tissue samples collected from 40 farms located in various states of Iran during 2008 – 2010 as part of a program to monitor Avian Influenza Viruses (AIV) infection. To determine the genetic relationship of Iranian viruses, the Hemagglutinin (HA) genes from ten isolates were amplified and sequenced (by RT-PCR method). Nucleotide sequences (orf) of the (HA) genes were used for phylogenetic tree construction. Deduced amino acid sequences showed the presence of L226 (234 in H9 numbering) in all ten Iranian isolates which indicates a preference to binding of α (2–6) sialic acid receptors, so these Iranian H9N2 viruses have the potential to infect human beings. These isolates showed high degree of homology with 2 human H9N2 isolates A/HK/1073/99, A/HK/1074/99. Phylogenetic analysis of showed that all the HA genes of the Iranian H9N2 viruses fall into a single group within a G1-like sublineage which had contributed as donor of six internal genes to H5N1 highly pathogenic avian influenza. The results of this study indicated that all Iranian viruses have the potential to emerge as highly pathogenic influenza virus, and considering the homology of these isolates with human H9N2 strains, it seems that the potential of these avian influenza isolates to infect human should not be overlooked.

Keywords: influenza virus, hemagglutinin, neuraminidase, Iran

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Authors: Irina S. Kolesnikova, Alexander A. Dolskiy, Natalya A. Lemskaya, Yulia V. Maksimova, Asia R. Shorina, Alena S. Telepova, Alexander S. Graphodatsky, Dmitry V. Yudkin

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A cytogenetic and molecular genetic study of the family with a male child who had mental retardation and autistic features revealed an abnormal chromosome 13 bearing an enlarged p-arm with amplified ribosomal DNA (rDNA) in a boy and his father. Cytogenetic analysis using standard G-banding and FISH with labeled rDNA probes revealed an abnormal chromosome 13 with an enlarged p-arms due to rDNA amplification in a male child, who had clinically confirmed mental retardation and an autistic behavior. This chromosome is evidently inherited from the father, who has morphologically the same chromosome, but is healthy. The karyotype of the mother was normal. Ag-NOR staining showed brightly stained large whole-p-arm nucleolus organizer regions (NORs) in a child and normal-sized NORs in his father with 13p+-NOR-amount mosaicism. qRT-PCR with specific primers showed highly increased levels of 18S, 28S and 5,8 S ribosomal RNA (rRNA) in the patient’s blood samples compared to a normal healthy control donor. Both patient’s father and mother had no elevated levels of rRNAs expression. Thus, in this case, rRNA level seems to correlate with mental retardation in familial individuals with 13p+. Our findings of rRNA overexpression in a patient with mental retardation and his parents may show a possible link between the karyotype (p-arm enlargement due to rDNA amplification), rDNA functionality (rRNA overexpression), functional changes in the brain and mental retardation. The study is supported by Russian Science Foundation Grant 15-15-10001.

Keywords: mental retardation, ribosomal DNA–rDNA, ribosomal RNA–rRNA, nucleolus organizer region–NOR, chromosome 13

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Authors: Akintunde Alo

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The incessant encroachments into the forest ecosystem by the farmers and local contractors constitute a major threat to the conservation of genetic resources and biodiversity in Nigeria. To propose a viable monitoring system, this study employed Geographic Information System (GIS) technology to assess the changes that occurred for a period of five years (between 2011 and 2016) in Ikere forest reserve. Landsat imagery of the forest reserve was obtained. For the purpose of geo-referencing the acquired satellite imagery, ground-truth coordinates of some benchmark places within the forest reserve was relied on. Supervised classification algorithm, image processing, vectorization and map production were realized using ArcGIS. Various land use systems within the forest ecosystem were digitized into polygons of different types and colours for 2011 and 2016, roads were represented with lines of different thickness and colours. Of the six land-use delineated, the grassland increased from 26.50 % in 2011 to 45.53% in 2016 of the total land area with a percentage change of 71.81 %. Plantations of Gmelina arborea and Tectona grandis on the other hand reduced from 62.16 % in 2011 to 27.41% in 2016. The farmland and degraded land recorded percentage change of about 176.80 % and 8.70 % respectively from 2011 to 2016. Overall, the rate of deforestation in the study area is on the increase and becoming severe. About 72.59% of the total land area has been converted to non-forestry uses while the remnant 27.41% is occupied by plantations of Gmelina arborea and Tectona grandis. Interestingly, over 55 % of the plantation area in 2011 has changed to grassland, or converted to farmland and degraded land in 2016. The rate of change over time was about 9.79 % annually. Based on the results, rapid actions to prevail on the encroachers to stop deforestation and encouraged re-afforestation in the study area are recommended.

Keywords: land use change, forest reserve, satellite imagery, geographical information system

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Authors: Rotimi Sunday Ajani, Omowumi Femi-Akinlosotu

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Background: The usually paired human kidneys are retroperitoneal urinary organs with some endocrine functions. Standard text books of anatomy ascribe single value to each of the dimension of length, width and thickness. Research questions: These values do not give consideration to racial and genetic variability in human morphology. They may thus be erroneous to students and clinicians working on Nigerians. Objectives: The study aimed at establishing reference values of the kidney length, width and thickness for Nigerians using the cadaveric model. Methodology: The length, width, thickness and weight of sixty kidneys harvested from cadavers of thirty adult Nigerians (Male: Female; 27: 3) were measured. Respective volume was calculated using the ellipsoid formula. Results: The mean length of the kidney was 9.84±0.89 cm (9.63±0.88 {right}; 10.06±0.86 {left}), width- 5.18±0.70 cm (5.21±0.72 {right}; 5.14±0.70 {left}), thickness-3.45±0.56 cm (3.36±0.58 {right}, 3.53±0.55 {left}), weight-125.06±22.34 g (122.36±21.70 {right}; 127.76 ±24.02 {left}) and volume of 95.45± 24.40 cm3 (91.73± 26.84 {right}; 99.17± 25.75 {left}). Discussion: Though the values of the parameters measured were higher for the left kidney (except for the width), they were not statistically significant. The various parameters obtained by this study differ from those of similar studies from other continents. Conclusion: Stating single value for each of the parameter of length, width and thickness of the kidney as currently obtained in textbooks of anatomy may be incomplete information and hence misleading. Thus, there is the need to emphasize racial differences when stating the normal values of kidney dimensions in textbooks of anatomy. Implication for Research and Innovation: The results of the study showed the dimensions of the kidney (length, width and thickness) have interracial vagaries as they were different from those of similar studies and values stated in standard textbooks of human anatomy. Future direction: This is a preliminary report and the study will continue so that more data will be obtained.

Keywords: kidney dimensions, cadaveric estimation, adult nigerians, racial differences

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Authors: Michael G. Pantelyat

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Fundamental basics of pure and applied research in the area of magneto-thermo-mechanical numerical analysis and design of innovative electromagnetic devices (modern induction heaters, novel thermoelastic actuators, rotating electrical machines, induction cookers, electrophysical devices) are elaborated. Thus, mathematical models of magneto-thermo-mechanical processes in electromagnetic devices taking into account main interactions of interrelated phenomena are developed. In addition, graphical representation of coupled (multiphysics) phenomena under consideration is proposed. Besides, numerical techniques for nonlinear problems solution are developed. On this base, effective numerical algorithms for solution of actual problems of practical interest are proposed, validated and implemented in applied 2D and 3D computer codes developed. Many applied problems of practical interest regarding modern electrical engineering devices are numerically solved. Investigations of the influences of various interrelated physical phenomena (temperature dependences of material properties, thermal radiation, conditions of convective heat transfer, contact phenomena, etc.) on the accuracy of the electromagnetic, thermal and structural analyses are conducted. Important practical recommendations on the choice of rational structures, materials and operation modes of electromagnetic devices under consideration are proposed and implemented in industry.

Keywords: electromagnetic devices, multiphysics, numerical analysis, simulation and design

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Davit Shahnazaryan, Diogo Gomes, Mher Safaryan

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Many symbolic computations and manipulations required in the analysis of partial differential equations (PDE) or systems of PDEs are tedious and error-prone. These computations arise when determining conservation laws, entropies or integral identities, which are essential tools for the study of PDEs. Here, we discuss a new Mathematica package for the symbolic analysis of PDEs that automate multiple tasks, saving time and effort. Methodologies: During the research, we have used concepts of linear algebra and partial differential equations. We have been working on creating algorithms based on theoretical mathematics to find results mentioned below. Major Findings: Our package provides the following functionalities; finding symmetry group of different PDE systems, generation of polynomials invariant with respect to different symmetry groups; simplification of integral quantities by integration by parts and null Lagrangian cleaning, computing general forms of expressions by integration by parts; finding equivalent forms of an integral expression that are simpler or more symmetric form; determining necessary and sufficient conditions on the coefficients for the positivity of a given symbolic expression. Conclusion: Using this package, we can simplify integral identities, find conserved and dissipated quantities of time-dependent PDE or system of PDEs. Some examples in the theory of mean-field games and semiconductor equations are discussed.

Keywords: partial differential equations, symbolic computation, conserved and dissipated quantities, mathematica

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Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

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A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: critical path, transportation network, connectivity reliability, network model, Neo4j application, edge betweenness centrality index

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Authors: Mohammed A. Alshehri

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Salvia officinalis is an aromatic plant member of the mint (Labiatae) family. It is popular kitchen herb. Not surprise to find that the name of this herb related to cure, in Latin language Salvia means to cure where officinalis means medicinal which answer why the sage has a top place in the list of medicinal plants. The aim of the present study was to assess the genetic damage and cytological changes caused by exposure of the test organism (Rattusrattus) to Salvia officinals. For this purpose, adult female rats, weighing 200–250 g, were used as donors. A total of 36 adult Wister male rats were randomly assigned to five groups: the experimental groups (rats were intraperitonealy injected with Salvia officinalis pure extract at (0.1, 0.2, 0.5, 0.1mg/kg body weight, the same dose was administered once a day. Control group (rats were injected intraperitonealy physiological saline. And positive control were injected with Cyclophosphamide. On the 21st days following Salvia officinalis pure extract exposure, rats were sacrificed, and samples of bone marrow were collected. Following that, we performed a micronuclei (MN) test using MNNCE (Micro-nucleated normocromatic erythrocytes) and MNPCE (Micronucleated polychromatic erythrocytes), NDI (Nuclear division index), and cytological parameters using NDCI (nuclear division cytotoxicity index), necrotic, and apoptotic cells in rat's bone marrow samples. Results showed that there was a no significant increase in the frequency of micro-nucleatedas well as in cytological parameters in bone marrow cells. In light of these results, if Salvia officinalis pure extract may considered to be safe from the stand point of genotoxicity and cytotoxicity effects.

Keywords: Salvia officinalis, micronucleus, NDI, NDCI, toxicity, chromosomal aberrations

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Authors: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

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Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

Procedia PDF Downloads 342

Authors: Rajani S. Pujar, Pandurangarao N. Kulkarni

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Increased temporal masking affects the speech perception in persons with sensorineural hearing impairment especially under adverse listening conditions. This paper presents a cascaded scheme, which employs a noise reduction algorithm as well as temporal splitting of the speech signal. Earlier investigations have shown that by splitting the speech temporally and presenting alternate segments to the two ears help in reducing the effect of temporal masking. In this technique, the speech signal is processed by two fading functions, complementary to each other, and presented to left and right ears for binaural dichotic presentation. In the present study, half cosine signal is used as a fading function with crossover gain of 6 dB for the perceptual balance of loudness. Temporal splitting is combined with noise reduction algorithm to improve speech perception in the background noise. Two noise reduction schemes, namely spectral subtraction and Wiener filter are used. Listening tests were conducted on six normal-hearing subjects, with sensorineural loss simulated by adding broadband noise to the speech signal at different signal-to-noise ratios (∞, 3, 0, and -3 dB). Objective evaluation using PESQ was also carried out. The MOS score for VCV syllable /asha/ for SNR values of ∞, 3, 0, and -3 dB were 5, 4.46, 4.4 and 4.05 respectively, while the corresponding MOS scores for unprocessed speech were 5, 1.2, 0.9 and 0.65, indicating significant improvement in the perceived speech quality for the proposed scheme compared to the unprocessed speech.

Keywords: MOS, PESQ, spectral subtraction, temporal splitting, wiener filter

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Authors: Christian Arcos, Marley Vellasco, Abraham Alcaim

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In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario.

Keywords: binary labels, local binary patterns, mask, wavelet coefficients, speech enhancement, speech recognition

Procedia PDF Downloads 226