Movie trailers are prepared using a one-size-fits-all framework. These days, however, streaming platforms seek to overcome this problem and provide personalized trailers via the investigation of centralized server-side solutions. This can be achieved by analyzing personal user data, and can lead to two major issues: privacy violation and enormous demand in computational resources. This paper proposes an innovative, low-power, client-driven method to facilitate the personalized trailer generation process. It tackles the complex process of detecting personalized actions in real-time from lightweight thumbnail containers. The HTTP live streaming (HLS) server and client are locally configured to validate the proposed method. The system is designed to support a wide range of client hardware with different computational capabilities and has the flexibility to adapt to network conditions. To test the effectiveness of this method, twenty-five broadcast movies, specifically in the western and sports genres, are evaluated. To the best of our knowledge, this is the first-ever client-driven framework that uses thumbnail containers as input to facilitate the trailer generation process.

Mobile devices have been increased exceptionally in recent years, consequently data generation has also been raised exceptionally. Most of the data generated by mobile devices is transferred to servers for processing and storage. Managing security of mobile data is a necessary feature of every network and mostly encryption is used to avoid security breaches. The major challenge is that, mobile devices are very small with shortage of resources, on the other hand encryption of data requires extra energy. It is necessary to minimize energy requirements for encryption of data. For this experimental research, an android based application is developed, which optimize energy requirements for both single and double encryption techniques. AES and Blowfish encryption algorithms are used with different files sizes to test the energy requirements for single encryption, it is also examined that energy consumed by Blowfish is 119.311% more than AES. For double encryption methods, AES-Blowfish, Blowfish-AES and XTS-AES combinations of algorithms are used and energy usage is gathered. In double encryption XTS-AES consumed 13.26% less power consumption as compared to AES-Blowfish and 44.97% less then Blowfish-AES combination methods. Results of experiments revealed that AES is more energy efficient for single encryption and for double encryption XTS-AES combination requires less energy.

Background: The popularity and proliferation of online social networks (OSNs) have created massive social interaction among users that generate an extensive amount of data. An OSN offers a unique opportunity for studying and understanding social interaction and communication among far larger populations now more than ever before. Recently, OSNs have received considerable attention as a possible tool to track a pandemic because they can provide an almost real-time surveillance system at a less costly rate than traditional surveillance systems. Methods: A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment. Result: OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics. Conclusions: OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data. (C) 2016 Elsevier Inc. All rights reserved.

Background: Dengue virus is the causative agent of dengue fever, a vector borne infection which causes selflimiting to life threatening disease in humans. A sero-epidemiological study was conducted to understand the current epidemiology of dengue virus in Pakistan which is now known as a dengue endemic country after its first reported outbreak in 1994. Methods: To investigate the prevalence of dengue virus in Pakistan during 2009-2014, a total of 9,493 blood samples were screened for the detection of anti-dengue IgM antibodies using ELISA. Clinical and demographic features available with hospital records were reviewed to ascertain mortalities related to dengue hemorrhagic shock syndrome. Results: Out of 9,493 samples tested, 37% (3,504) were found positive for anti-dengue IgM antibodies. Of the seropositive cases, 73.6% (2,578/3,504) were male and 26.4% (926/3,504) were female. The highest number (382/929; 41.1%) of sero-positive cases was observed among the individuals of age group 31-40 years. The highest number of symptomatic cases was reported in October (46%; 4,400/9,493), and the highest number of sero-positive cases among symptomatic cases was observed in November (45.7%; 806/1,764). Mean annual patient incidence (MAPI) during 2009-2014 in Pakistan remained 0.30 with the highest annual patient incidence (11.03) found in Islamabad. According to the available medical case record, 472 dengue related deaths were reported during 2009-2014. Conclusion: The data from earlier reports in Pakistan described the dengue virus incidence from limited areas of the country. Our findings are important considering the testing of clinical samples at a larger scale covering patients of vast geographical regions and warrants timely implementation of dengue vector surveillance and control programs.

The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

Speaker identification refers to the process of recognizing human voice using artificial intelligence techniques. Speaker identification technologies are widely applied in voice authentication, security and surveillance, electronic voice eavesdropping, and identity verification. In the speaker identification process, extracting discriminative and salient features from speaker utterances is an important task to accurately identify speakers. Various features for speaker identification have been recently proposed by researchers. Most studies on speaker identification have utilized short-time features, such as perceptual linear predictive (PLP) coefficients and Mel frequency cepstral coefficients (MFCC), due to their capability to capture the repetitive nature and efficiency of signals. Various studies have shown the effectiveness of MFCC features in correctly identifying speakers. However, the performances of these features degrade on complex speech datasets, and therefore, these features fail to accurately identify speaker characteristics. To address this problem, this study proposes a novel fusion of MFCC and time-based features (MFCCT), which combines the effectiveness of MFCC and time-domain features to improve the accuracy of text-independent speaker identification (SI) systems. The extracted MFCCT features were fed as input to a deep neural network (DNN) to construct the speaker identification model. Results showed that the proposed MFCCT features coupled with DNN outperformed existing baseline MFCC and time-domain features on the LibriSpeech dataset. In addition, DNN obtained better classification results compared with five machine learning algorithms that were recently utilized in speaker recognition. Moreover, this study evaluated the effectiveness of one-level and two-level classification methods for speaker identification. The experimental results showed that two-level classification presented better results than one-level classification. The proposed features and classification model for identifying a speaker can be widely applied to different types of speaker datasets.

Mutations in PJVK, encoding Pejvakin, cause autosomal recessive nonsyndromic hearing loss in humans at the DFNB59 locus on chromosome 2q31.2. Pejvakin is involved in generating auditory and neural signals in the inner ear. We have identified a consanguineous Pakistani family segregating sensorineural progressive hearing loss as a recessive trait, consistent with linkage to DFNB59. We sequenced PJVK and identified a novel missense mutation, c.1028G>C in exon 7 (p.C343S) co-segregating with the phenotype in the family. The p.C343 residue is fully conserved among orthologs from different vertebrate species. We have also determined that mutations in PJVK are not a common cause of hearing loss in families with moderate to severe hearing loss in Pakistan. This is the first report of PJVK mutation in a Pakistani family and pinpoints an important residue for PJVK function. (c) 2012 Elsevier B.V. All rights reserved.

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

Background: An accurate and automatic computer-aided multi-class decision support system to classify the magnetic resonance imaging (MRI) scans of the human brain as normal, Alzheimer, AIDS, cerebral calcinosis, glioma, or metastatic, which helps the radiologists to diagnose the disease in brain MRIs is created. Methods: The performance of the proposed system is validated by using benchmark MRI datasets (OASIS and Harvard) of 310 patients. Master features of the images are extracted using a fast discrete wavelet transform (DWT), then these discriminative features are further analysed by principal component analysis (PCA). Different subset sizes of principal feature vectors are provided to five different decision models. The classification models include the J48 decision tree, k-nearest neighbour (kNN), random forest (RF), and least-squares support vector machine (LS-SVM) with polynomial and radial basis kernels. Results: The RF-based classifier outperformed among all compared decision models and achieved an average accuracy of 96% with 4% standard deviation, and an area under the receiver operating characteristic (ROC) curve of 99%. LS-SVM (RBF) also shows promising results (i.e., 89% accuracy) when the least number of principal features was used. Furthermore, the performance of each classifier on different subset sizes of principal features was (80%-96%) for most performance metrics. Conclusion: The presented medical decision support system demonstrates the potential proof for accurate multi-class classification of brain abnormalities; therefore, it has a potential to use as a diagnostic tool for the medical practitioners.

Rotavirus A species (RVA) is the leading cause of severe diarrhea among children in both developed and developing countries. Among different RVA G types, humans are most commonly infected with G1, G2, G3, G4 and G9. During 2003-2004, G3 rotavirus termed as "new variant G3" emerged in Japan that later disseminated to multiple countries across the world. Although G3 rotaviruses are now commonly detected globally, they have been rarely reported from Pakistan. We investigated the genetic diversity of G3 strains responsible RVA gastroenteritis in children hospitalized in Rawalpindi, Pakistan during 2014. G3P[8] (18.3%; n =3D 24) was detected as the most common genotype causing majority of infections in children less than 06 months. Phylogenetic analysis of Pakistani G3 strains showed high amino acid similarity to "new variant G3" and G3 strains reported from China, Russia, USA, Japan, Belgium and Hungary during 2007-2012. Pakistani G3 strains belonged to lineage 3 within sub-lineage 3d, containing an extra N-linked glycosylation site compared to the G3 strain of RotaTeqTM. To our knowledge, this is the first report on the molecular epidemiology of G3 rotavirus strains from Pakistan and calls for immediate response measures to introduce RV vaccine in the routine immunization program of the country on priority.=20

The potential of microalgae as an alternative energy source has been adequately studied. However, exclusive use of microalgae as an energy feedstocks cannot warrant their scalability and economical sustainability due to the high cost involved in their biomass processing. The co-processing of microalgae biomass with other related bio-refinery applications can offset their cost and improve their sustainability. Thus, it triggers up the need of exploring the potential of microalgae biomass beyond their typical use. Microalgae offer interesting features to qualify them as alternative feedstocks for various bio-refinery applications. Microalgae have unique abilities to utilize them for industrial and environmental applications. Thus, this review discusses to expand the scope of integrating microalgae with other biotechnological applications to enhance their sustainability. The use of microalgae as a feed for animal and aquaculture, fertilizers, medicine, cosmetic, environmental and other biotechnological applications is thoroughly reviewed. It also highlights the barriers, opportunities, developments, and prospects of extending the scope of microalgae. This study concludes that sustained research funding, and a shift of microalgae focus from biofuels production to bio-refinery co-products can qualify them as promising feedstocks. Moreover, technology integration is inevitable to off-set the cost of microalgae biomass processing. It is expected that this study would be helpful to determine the future role of microalgae in bio-refinery applications.

Variants of KCNQ4 are one of the most common causes of dominantly inherited nonsyndromic hearing loss. We investigated a consanguineous family in which two individuals had prelignual hearing loss, apparently inherited in a recessive mode. Whole-exome sequencing analyses demonstrated genetic heterogeneity as variants in two different genes segregated with the phenotype in two branches of the family. Members in one branch were homozygous for a pathogenic variant of TMC1. The other two affected individuals were homozygous for a missense pathogenic variant in KCNQ4 c.872C > T; p.(Pro291Leu). These two individuals had prelingual, progressive moderate to severe hearing loss, while a heterozygous carrier had late onset mild hearing loss. Our work demonstrates that p.Pro2911 variant is semi-dominantly inherited. This is the first report of semi-dominance of a KCNQ4 variant.