Assessment of seismic hazard of roller compacted concrete dam site in Gilgit-Baltistan of northern Pakistan

The proposed site of the Diamer Bhasha Dam in northern Pakistan is situated in an active tectonic zone with intensive seismicity,which makes it necessary for seismic hazard analysis(SHA).Deterministic and probabilistic approaches have been used for SHA of the dam site.The Main Mantle Thrust(MMT),Main Karakaram Thrust(MKT),Raikot-Sassi Fault(RKSF)and Kohistan Fault(KF)have been considered as major seismic sources,all of which can create maximum ground shaking with maximum potential earthquake(MPE).Deterministically estimated MPE for magnitudes of 7.8,7.7,7.6,and 7.1 can be produced from MMT,MKT,RKSF and KF,respectively.The corresponding peak ground accelerations(PGA)of 0.07,0.11,0.13 and 0.05 g can also be generated from these earthquakes,respectively.The deterministic analysis predicts a so-called floating earthquake as a MPE of magnitude=7.1 as close as 10 km away from the site.The corresponding PGA was computed as 0.38 g for a maximum design earthquake at the project site.However,the probabilistic analysis revealed that the PGA with 50%probability of exceedance in 100 years is 0.18 g.Thus,this PGA value related to the operational basis earthquake(OBE)is suggested for the design of this project with shear wave velocity(V_(s30))equal to 760 m/s under dense soil and soft rock conditions.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine,the realization of reversible watermarking through information security is an emerging research field.However,adding watermarks hinders the distribution of pixels in the cover image because it creates distortions(which lead to an increase in the detection probability).In this article,we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security.The proposed method selects two adjacent gray pixels whose least significant bit(LSB)is different from the relevant message bit and then calculates the distortion degree.We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values.Experimental results show that the designed method is robust to different attacks and has a high PSNR(peak signal-to-noise ratio)value.The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB.In addition,the proposed algorithm is tested against the latest technology on standard images,and it is found that the average PSNR of our proposed reversible watermarking technology is higher(i.e.,51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.

ETL Maturity Model for Data Warehouse Systems:A CMMI Compliant Framework

The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration(VLSI)technology and design software have significantly broadened the scope of digital signal processing(DSP)applications.The use of application-specific integrated circuits(ASICs)and programmable digital signal processors for many DSP applications have changed,even though new system implementations based on reconfigurable computing are becoming more complex.Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation(DWT)and sophisticated computerized design techniques,which are much needed in today’s modern world.New research and commercial efforts to sustain power optimization,cost savings,and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged.Hence,in this paper,it is proposed that theDWTmethod can be implemented on a fieldprogrammable gate array in a digital architecture(FPGA-DA).We examined the effects of quantization on DWTperformance in classification problems to demonstrate its reliability concerning fixed-point math implementations.The Advanced Encryption Standard(AES)algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks(ANN)method.By reducing hardware area by 57%,the proposed system has a higher throughput rate of 88.72%,reliability analysis of 95.5%compared to the other standard methods.

Noisy ECG Signal Data Transformation to Augment Classification Accuracy

In this era of electronic health,healthcare data is very important because it contains information about human survival.In addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous monitoring.In this case,the data related to the heart is more important and requires proper analysis.For the analysis of heart data,Electrocardiogram(ECG)is used.In this work,machine learning techniques,such as adaptive boosting(AdaBoost)is used for detecting normal sinus rhythm,atrial fibrillation(AF),and noise in ECG signals to improve the classification accuracy.The proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF,and classifies other signals as normal,other,or noise.This article derives different features from the signal using Maximal Information Coefficient(MIC)and minimum Redundancy Maximum Relevance(mRMR)technique,and then classifies them based on their attributes.Since the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section(SOS)(filter)and correctly classify them.Several features were extracted to improve the detection of ECG data.Compared with existing methods,this work gives promising results and can help improve the classification accuracy of the ECG signals.

Ontology Driven Testing Strategies for IoT Applications

Internet-of-Things(IoT)has attained a major share in embedded software development.The new era of specialized intelligent systems requires adaptation of customized software engineering approaches.Currently,software engineering has merged the development phases with the technologies provided by industrial automation.The improvements are still required in testing phase for the software developed to IoT solutions.This research aims to assist in developing the testing strategies for IoT applications,therein ontology has been adopted as a knowledge representation technique to different software engineering processes.The proposed ontological model renders 101 methodology by using Protégé.After completion,the ontology was evaluated in three-dimensional view by the domain experts of software testing,IoT and ontology engineering.Satisfied results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for improvements.The Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.

Efficient Joint Key Authentication Model in E-Healthcare

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and smartphones.These devices and mobile applications are now increasingly used and integrated through the medical Internet of Things(mIoT).mIoT is an important part of the digital transformation of healthcare,because it can introduce new business models and allow efficiency improvements,cost control and improve patient experience.In the mIoT system,when migrating from traditional medical services to electronic medical services,patient protection and privacy are the priorities of each stakeholder.Therefore,it is recommended to use different user authentication and authorization methods to improve security and privacy.In this paper,our prosed model involves a shared identity verification process with different situations in the e-health system.We aim to reduce the strict and formal specification of the joint key authentication model.We use the AVISPA tool to verify through the wellknown HLPSL specification language to develop user authentication and smart card use cases in a user-friendly environment.Our model has economic and strategic advantages for healthcare organizations and healthcare workers.The medical staff can increase their knowledge and ability to analyze medical data more easily.Our model can continuously track health indicators to automatically manage treatments and monitor health data in real time.Further,it can help customers prevent chronic diseases with the enhanced cognitive functions support.The necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.

Temporal Preferences-Based Utility Control for Smart Homes

The residential sector contributes a large part of the energy to the global energy balance.To date,housing demand has mostly been uncontrollable and inelastic to grid conditions.Analyzing the performance of a home energy manage-ment system requires the creation of various profiles of real-world residential demand,as residential demand is complex and includes multiple factors such as occupancy,climate,user preferences,and appliance types.Average Peak Ratio(A2P)is one of the most important parameters when managing an efficient and cost-effective energy system.At the household level,the larger relative magni-tudes of certain energy devices make managing this ratio critical,albeit difficult.Various Demand Response(DR)and Demand Side Management(DSM)systems have been proposed to reduce this ratio to 1.The main ways to achieve this are economic incentives,user comfort modeling and control,or preference-based.In this study,we propose a unique opportunistic social time approach called the Time Utility Based Control Feature(TUBCF),which uses the concept of a utility function from economics to model and control consumer devices.We propose a DR model for residential customers to reduce Peak-to-Average Ratio(PAR)and improve customer satisfaction by eliminating Appliance Wait Time(WTA)during peak periods.For PAR reduction and WTA,we propose a system architecture and mathematical formulation.Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics.Simulation results show that using this strategy can reduce A2P by 80%and improve user comfort during peak hours.

Sustainable Learning of Computer Programming Languages Using Mind Mapping

In the current era of information technology,students need to learn modern programming languages efficiently.The art of teaching/learning program-ming requires many logical and conceptual skills.So it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efficiently.Mind mapping is a useful visual tool for establishing ideas and connecting them to solve problems.This research proposed an effective way to teach programming languages through visual tools.This experimental study uses a mind mapping tool to teach two programming environments:Text-based Programming and Blocks-based Programming.We performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Pacific region.Four different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this purpose.The results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.

Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion

In this paper, we have used the distributed mean value analysis (DMVA) technique with the help of random observe property (ROP) and palm probabilities to improve the network queuing system throughput. In such networks, where finding the complete communication path from source to destination, especially when these nodes are not in the same region while sending data between two nodes. So, an algorithm is developed for single and multi-server centers which give more interesting and successful results. The network is designed by a closed queuing network model and we will use mean value analysis to determine the network throughput (b) for its different values. For certain chosen values of parameters involved in this model, we found that the maximum network throughput for β≥0.7?remains consistent in a single server case, while in multi-server case for β≥ 0.5?throughput surpass the Marko chain queuing system.

Improving Network Efficiency by Selecting and Modifying Congestion Control Constraints

Congestion in wired networks not only causes severe information loss but also degrades overall network performance. To cope with the issue of network efficiency, in this paper we have pro- posed and investigated an efficient mechanism for congestion control by the selection of appropri- ate congestion window size and proactive congestion avoidance, which improves system overall performance and efficiency. The main objective of this work is to choose the accurate size of con- gestion window based on available link bandwidth and round trip time (RTT) in cross and grid topologies, instead of choosing number of hops (Previous researches), we have achieved significant improvement in the overall performance of the network. General simulation results under distinctive congestion scenarios are presented to illuminate the distinguished performance of the proposed mechanism.

Ensemble Modeling for the Classification of Birth Data

Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in healthcare.Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results.Using ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better decisions.It is observed that there is a huge difference between women depending on the region and their social lives.Due to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected child.In this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base learners.It is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual classifiers.ML algorithms are more accurate due to ensemble models,which reduce variance and classification errors.It is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the datasets.Developing such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.