Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.

The Odd-Point Ternary Approximating Schemes

We present a general formula to generate the family of odd-point ternary approximating subdivision schemes with a shape parameter for describing curves. The influence of parameter to the limit curves and the sufficient conditions of the continuities from C0 to C5 of 3- and 5-point schemes are discussed. Our family of 3-point and 5-point ternary schemes has higher order of derivative continuity than the family of 3-point and 5-point schemes presented by [Jian-ao Lian, On a-ary subdivision for curve design: II. 3-point and 5-point interpolatory schemes, Applications and Applied Mathematics: An International Journal, 3(2), 2008, 176-187]. Moreover, a 3-point ternary cubic B-spline is special case of our family of 3-point ternary scheme. The visual quality of schemes with examples is also demonstrated.

An Efficient and Reliable Multicasting for Smart Cities

The Internet of thing(IoT)is a growing concept for smart cities,and it is compulsory to communicate data between different networks and devices.In the IoT,communication should be rapid with less delay and overhead.For this purpose,flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead,energy consumption and packet drop etc.This paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT concept.In this paper,multicasting and unicasting is used for IoT in smart cities within a receiver-initiated mesh-based topology to disseminate the data to the cluster head.Smart cities networks are divided into cluster head,and each cluster head or core node will be responsible for transferring data to the desired receiver.This protocol is a novel approach according to the best of our knowledge,and it proves to be very useful due to its efficiency and reliability in smart cities concept because IoT is a collection of devices and having a similar interest for transmission of data.The results are implemented in Network simulator 2(NS-2).The result shows that the proposed protocol shows performance in overhead,throughput,packet drop,delay and energy consumption as compared to benchmark schemes.

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Education sector has witnessed several changes in the recent past.These changes have forced private universities into fierce competition with each other to get more students enrolled.This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands.To get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their competitors.This study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the universities.These techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore Pakistan.Experimental results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local universities.Based on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

Man Overboard Detection System Using IoT for Navigation Model

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry,roro,catamaran,frigate,yacht that are the vehicles services for the purpose of logistic and passenger transport.In this paper,all processes in the event of Man overboard(MOB)are initiated for smart transportation.In MOB the falling person is totally dependent on the person who first saw the falling person.The main objective of this paper is to develop a solution to this significant problem.If a staff member or a passenger does not see the fall into the sea,undesirable situations such as disappearance,injury and death can occur during the period until the absence of the fallen person is noticed.In this paper,a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements,in which human resources are intensively involved like passengers,freight,logistics,fishing,business,yacht,leisure and naval vessels.In this case,if the ship’s personnel or passengers fall into the sea in any way,it detected the fallen person into the sea by the sensors in the portable emergency device,which each person will carry.The warning system is activated via the in-ship automation system to which the information is transmitted by wireless communication.Thus,the case of MOB will be determined quickly.Internet of things(IoT)has a key role in identifying the location and information of the person falling into the sea through sensors,radio frequency,GPS and connected devices.Simultaneously,the alarm system on board will be activated and MOB flag(Oscar)will automatically be opened.This paper enables the Search and rescue(SAR)operations to be initiated and accelerated without losing time through decision-making process.

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Wireless Sensor Network(WSN)is considered to be one of the fundamental technologies employed in the Internet of things(IoT);hence,enabling diverse applications for carrying out real-time observations.Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks.A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks.Considering networks to be a type of graph,this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph.This idea was extended to the concept of edge metric dimension of a graph G,which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network.Regular plane networks can be easily constructed by repeating regular polygons.This design is of extreme importance as it yields high overall performance;hence,it can be used in various networking and IoT domains.The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks.In this paper,it is proved that the minimum landmarks required for the honeycomb network HC(n),and the hexagonal network HX(n)are 3 and 6 respectively.The bounds for the landmarks required for the hex-derived network HDN1(n)are also proposed.

Performance Evaluation of Topological Infrastructure in Internet-of-Things-Enabled Serious Games

Serious games have recently enticed many researchers due to their wide range of capabilities.A serious game is a mean of gaming for a serious job such as healthcare,education,and entertainment purposes.With the advancement in the Internet of Things,new research directions are paving the way in serious games.However,the internet connectivity of players in Internetof-things-enabled serious games is a matter of concern and has been worth investigating.Different studies on topologies,frameworks,and architecture of communication technologies are conducted to integrate them with serious games on machine and network levels.However,the Internet of things,whose core requirement is the provision of connectivity on the application layer,has different challenges for more dynamic applications such as serious games.The performance of Internet-of-things-enabled serious games depends on the type of infrastructure(wired,wireless)network and Mobile Ad-hoc Network(MANET)and is subtly different from one type of network to another.This paper investigates the connectivity challenges in the Internet-of-thingsenabled serious games using the mentioned infrastructure and identifies the core requirements for these games.It also aims to evaluate various parameters such as reliability,scalability,response time,to name a few,with varying infrastructure and network types.Results highlight the preliminary infrastructure finding and highlight the core setup for which the games are deployed.Moreover,this work will be a steppingstone for architecting the connectivity in serious games in a typical smart space with many infrastructures such as wired networks,wireless networks,and MANET.

Intelligent Model for Predicting the Quality of Services Violation

Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs accountability.Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it.Cloud users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and speed.In this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and availability.This paper also compares Simple-PSO and Parallel MutantPSO.In simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming signals.It is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place,the signal having more strength under the influence of emotions effects the response accordingly.Current research inmultisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or depression.Therefore,there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or depression.In this study,two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or depression.It was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.

Mobile Devices Interface Adaptivity Using Ontologies

Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.

Construction and Application of Subdivision Surface Scheme Using Lagrange Interpolation Polynomial

This paper offers a general formula for surface subdivision rules for quad meshes by using 2-D Lagrange interpolating polynomial [1]. We also see that the result obtained is equivalent to the tensor product of (2N + 4)-point n-ary interpolating curve scheme for N ≥ 0 and n ≥ 2. The simple interpolatory subdivision scheme for quadrilateral nets with arbitrary topology is presented by L. Kobbelt [2], which can be directly calculated from the proposed formula. Furthermore, some characteristics and applications of the proposed work are also discussed.

A UNIFIED THREE POINT APPROXIMATING SUBDIVISION SCHEME

In this paper, we propose a three point approximating subdivision scheme, with three shape parameters, that unifies three different existing three point approximating schemes. Some sufficient conditions for subdivision curve C0 to C3 continuity and convergence of the scheme for generating tensor product surfaces for certain ranges of parameters by using Laurent polynomial method are discussed. The systems of curve and surface design based on our scheme have been developed successfully in garment CAD especially for clothes modelling.

A Unified Interpolating Subdivision Scheme for Curves/Surfaces by Using Newton Interpolating Polynomial

This paper presents a general formula for (2m + 2)-point n-ary interpolating subdivision scheme for curves for any?integer m ≥ 0 and n ≥ 2 by using Newton interpolating polynomial. As a consequence, the proposed work is extended for surface case, which is equivalent to the tensor product of above proposed curve case. These formulas merge several notorious curve/surface schemes. Furthermore, visual performance of the subdivision schemes is also presented.

Effect of interface on mid-infrared photothermal response of MoS2 thin film grown by pulsed laser deposition

This study reports on the mid-infrared （mid-IR） photothermal response of multilayer MoS2 thin films grown on crystalline （p-type silicon and c-axis- oriented single crystal sapphire） and amorphous （Si/SiO2 and Si/SiN） substrates by pulsed laser deposition （PLD）. The photothermal response of the MoS2 films is measured as the changes in the resistance of the MoS2 films when irradiated with a mid-IR （7 to 8.2 μm） source. We show that enhancing the temperature coefficient of resistance （TCR） of the MoS2 thin films is possible by controlling the film-substrate interface through a proper choice of substrate and growth conditions. The thin films grown by PLD are characterized using X-ray diffraction, Raman, atomic force microscopy, X-ray photoelectron microscopy, and transmission electron microscopy. The high-resolution transmission electron microscopy （HRTEM） images show that the MoS2 films grow on sapphire substrates in a layer-by-layer manner with misfit dislocations. The layer growth morphology is disrupted when the films are grown on substrates with a diamond cubic structure （e.g., silicon） because of twin growth formation. The growth morphology on amorphous substrates, such as Si/SiO2 or Si/SiN, is very different. The PLD-grown MoS2 films on silicon show higher TCR （-2.9% K^-1 at 296 K）, higher mid-IR sensitivity （△R/R = 5.2%）, and higher responsivity （8.7 V·W^-1） compared to both the PLD-grown films on other substrates and the mechanically exfoliated MoS2 flakes transferred to different substrates.