Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

ER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stage Opt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features.

The ßNiAl coating was deposited onto Nickel based CMSX-4 superalloy by in-situ CVD (Chemical Vapor Deposition) method. Main focus of this contribution was to study the influence of aluminizing time and temperature on the microstructure and thickness of the coating; this was followed by examination by XRD (X-Ray Diffraction), electron microscope. Results suggest that an incremental variation in temperature alters the coating activities from HA (High Activity) to LA (Low Activity). This is exhibited by the resultant CT (Coating Thickness) since HA coatings are thicker than LA counterparts. The microstructure of the coating formed at low temperature (or HA ones) showed a large amount of a-Cr precipitates while one formed at high temperature (or LA ones) exhibited lower amounts of such precipitates. Moreover, incremental aluminizing time showed linear trend of CT at initial stage, thereafter (10 hrs) it leveled off. Whereas it does not affect microstructure of the coating.

Schema merging is a process of integrating multiple data sources into a GCS (Global Conceptual Schema). It is pivotal to various application domains, like data ware housing and multi-databases. Schema merging requires the identification of corresponding elements, which is done through schema matching process. In this process, corresponding elements across multiple data sources are identified after the comparison of these data sources with each other. In this way, for a given set of data sources and the correspondence between them, different possibilities for creating GCS can be achieved. In applications like multi-databases and data warehousing, new data sources keep joining in and GCS relations are usually expanded horizontally or vertically. Schema merging approaches usually expand GCS relations horizontally or vertically as new data sources join in. As a result of such expansions, an unbalanced GCS is created which either produces too much NULL values in response to global queries or a result of too many Joins causes poor query processing. In this paper, a novel approach, TuSMe (Tuned Schema Merging) technique is introduced to overcome the above mentioned issue via developing a balanced GCS, which will be able to control both vertical and horizontal expansion of GCS relations. The approach employs a weighting mechanism in which the weights are assigned to individual attributes of GCS. These weights reflect the connectedness of GCS attributes in accordance with the attributes of the principle data sources. Moreover, the overall strength of the GCS could be scrutinized by combining these weights. A prototype implementation of TuSMe shows significant improvement against other contemporary state-of-the-art approaches.

IoT (Internet of Things) is the next generation of the Internet. The main goal of IoT is to connect each and every physical object to the Internet Cloud. This concept is introduced by bringing IoT technology to the laboratories, making expensive laboratory equipment available on-cloud for real-time experimentation. In this paper, an on CLP (Cloud Laboratory Platform) is presented by employing the concept of IoT to the academic experimentation environment. The CLP allows a rapid deployment of an online laboratory system enabling students and researchers to perform actual experiments on the on-Cloud laboratory equipment using a web interface. A web interface for end users to access front end of the system. This interface was developed for login purposes so that any user can perform experiments from anywhere. The interface also provides options for comments and feedback. Moreover, this research contribution also facilitate users to test their designs and record observations in real-time on the equipment. For demonstration purposes, a remote lab has been developed for high-tech Xilinx FPGA (Field Programmable Gate Array) development boards, namely Spartan-II and Spartan-III. This project aims to provide students a new tool to enhance their learning experience and encourage them to test their theoretical knowledge in practical applications.

WT (Wavelet Transform) is considered as landmark for image compression because it represents a signal in terms of functions which are localized both in frequency and time domain. Wavelet sub-band coding exploits the self-similarity of pixels in images and arranges resulting coefficients in different sub-bands. A much simpler and fully embedded codec algorithm SPIHT (Set Partitioning in Hierarchical Trees) is widely used for the compression of wavelet transformed images. It encodes the transformed coefficients depending upon their significance comparative to the given threshold. Statistical analysis reveals that the output bit-stream of SPIHT comprises of long trail of zeroes that can be further compressed, therefore SPIHT is not advocated to be used as sole mean of compression. In this paper, wavelet transformed images have been initially compressed by using SPIHT technique and to attain more compression, the output bit streams of SPIHT are then fed to entropy encoders; Huffman and Arithmetic encoders, for further de-correlation. The comparison of two concatenations has been carried out by evaluating few factors like Bit Saving Capability, PSNR (Peak Signal to Noise Ratio), Compression Ratio and Elapsed Time. The experimental results of these cascading demonstrate that SPIHT combined with Arithmetic coding yields better compression ratio as compared to SPIHT cascaded with Huffman coding. Whereas, SPIHT once combined with Huffman coding is proved to be comparatively efficient.

Software models describe structures, relationships and features of the software. Modern software development methodologies such as MDE (Model Driven Engineering) use models as core elements. In MDE, the code is automatically generated from the model and model errors can implicitly shift into the code, which are difficult to find and fix. Model verification is a promising solution to this problem. However, coverage of all facets of model verification is a painful job and existing formal/semi-formal verification methods are greatly inspired by mathematics and difficult to understand by the software practitioners. This work considers particularly UML Class/OCL (Unified Modeling Language Class/Object Constraint Language) model and presents an ontology-based verification method. In the proposed method, a class diagram is transformed into ontology specified in OWL (Web Ontology Language) and constraints into SPARQL NAF (Negation as Failure) queries. This work tries to demonstrate that the proposed approach can efficiently cover all aspects of UML Class/OCL model verification.

Identifying activities of daily living is an important area of research with applications in smart-homes and healthcare for elderly people. It is challenging due to reasons like human self-occlusion, complex natural environment and the human behavior when performing a complicated task. From psychological studies, we know that human gaze is closely linked with the thought process and we tend to “look” at the objects before acting on them. Hence, we have used the object information present in gaze images as the context and formed the basis for activity prediction. Our system is based on HMM (Hidden Markov Models) and trained using ANN (Artificial Neural Network). We begin with extracting motion information from TPV (Third Person Vision) streams and object information from FPV (First Person Vision) cameras. The advantage of having FPV is that the object information forms the context of the scene. When context is included as input to the HMM for activity recognition, the precision increases. For testing, we used two standard datasets from TUM (Technische Universitaet Muenchen) and GTEA Gaze+ (Georgia Tech Egocentric Activities). In the first round, we trained our ANNs only with activity information and in the second round added the object information as well. We saw a significant increase in the precision (and accuracy) of predicted activities from 55.21% (respectively 85.25%) to 77.61% (respectively 93.5%). This confirmed our initial hypothesis that including the focus of attention of the actor in the form of object seen in FPV can help in predicting activities better.

In this paper, energy and exergy analysis has been conducted on a subcritical coal fired power plant of Wisconsin Power and Light Company, USA to investigate the steam cycle energy and exergy efficiency. The cycle is analyzed by developing a mathematical model using its operating and design parameters. The analysis is performed using EES (Engineering Equation Solver). The energy analysis shows that major share of energy loss occurs in condenser i.e. 72% of total cycle energy loss, whereas, exergy analysis shows that 83.09% total exergy destruction of cycle occurs in boiler.Furthermore, the simulation results are compared with actual with an absolute error of 3.1%. Additionally, the parametric study is performed to examine the effects of various operating parameters such as main steam pressure and temperature, condenser pressure, terminal and drain cooler temperature difference on net power output, energy and exergy efficiency of cycle. The parametric study shows that the plant has maximum energy and exergy efficiencies at steam pressure of 2500psi, condenser pressure of 1.0psi and main steam temperature of 1100oF. Furthermore, these parameters do not seem to change energy and exergy efficiencies significantly.

The objective of this paper is to compile information that will help researchers and practitioners to use EFA (Exploratory Factor Analysis) in reference to the relevant anti-counterfeiting strategies in supply chain under deceptive buying environment, in order to make prudent decisions according to the “best industry practices” based on ISO (International Organization for Standardization) available in the automotive industry. Product counterfeiting is a worldwide business issue which effect firm’s supply chain (supply chain sustainability) directly or indirectly. The study used a quantitative research design technique where categorical and numerical variables are used to determine relationships among variables where a survey is used as a main research method. Futuristically, the developed instrument using EFA would lead to the CFA (Confirmatory Factor Analysis) to answer questions regarding validity of the instrument in the concerned population subgroups. This empirical study is done after conducting Initial Survey where pilot testing procedure of Initial instrument was followed to make sure that questions asked were good enough (reliability analysis) to capture the impacts of ANCT-INT (Anti-Counterfeiting Strategies Initiative) on the FSP (Firms’ Supply Performance). The Pilot test process of Initial Survey Instrument is conducted prior to an actual data collection (confirmatory survey) stage that can helps researcher in refining the confirmatory survey instrument. The pilot test is conducted under the patronage of PAAPAM (Pakistan Association of Automobile Parts and Accessories Manufacturers) secretariat in Lahore. The participants involved are the Automotive Engineering firms practicing as the permanent PAAPAM member firms.

Liberation size plays a significant role to select a correct and successful concentration method for natural iron ores. This paper aims to evaluate the liberation size of two newly discovered iron ores, namely Zard Koh and Kulli Koh, existing in Chagai region of Pakistan. Zard Koh iron ore is mainly composed of maghemite along with the pyrite, chlorite, grossular and admontite as gangue minerals, whereas, Kulli Koh iron ore is comprised of hematite mostly with quartz, dravite and kaolinite as the gangue minerals. The representative samples of ores were pulverized and sieved to different size fractions. The liberation size of iron bearing minerals and gangues was investigated by analyzing the different size fractions of each ore using XRF (X-Ray Fluorescence), SEM (Scanning Electron Microscope) attached with EDS (Energy Dispersive Spectroscope). The XRF results revealed that the most probable liberation size of Zard Koh and Kulli Koh ores is most likely existing at -75+45 and -150+106 ?m, respectively. In order to confirm this liberation size, further evidences were collected using SEM and EDS examinations. It is interesting to note that the results obtained from SEM and EDS were quite in agreement with XRF results.

As Pakistan along with the rest of the world continues to develop, demand for limited natural resources continues to increase also. This demand for resources and subsequent waste that is generated has driven the idea of sustainability towards the forefront of modern day research. To achieve this goal, new and innovative ways are being developed to recycle waste materials that otherwise would end up in landfill sites. Slag, a by-product of steel manufacturing is one such waste material. Pakistan as being a developing country does not have proper facilities to insure safe disposal and recycling of slag. Hence, new and innovative ways for recycling slag are a necessity for Pakistan to move towards technological advancement. Current study focuses to explore the feasibility of using local induction furnace slag as partial substitute of OPC (Ordinary Portland Cement) in concrete as well as to check its performance against chloride and sulphate damage. The test results showed that 15% or more slag replacement will make the concrete immune to chloride and sulphate damage. However, results also indicate that with increase in slag replacement percentage there is a significant drop in compressive, flexural and split tensile strength of concrete. Keeping in view the loss of strength, immunity against chloride and sulphate damage, reduction in cost of making concrete and sustainability benefits; 15% slag replacement has been deemed optimum replacement value.

The concentration of Cu (Copper) and Zn (Zinc) decreases upon flooded conditions of rice soil. To assess the effects of flooding and application of Cu and Zn coated urea on changes in Eh, pH and solubility of Cu and Zn, a glasshouse experiment was conducted at Universiti Putra Malaysia. Rice plants (30 days old seedlings of type MR-219) on two soils (riverine and alluvium and marine alluvium) were transplanted. Nine treatments with variable rates and combinations of Cu and Zn coated urea were applied. The sources of fertilizers were copper sulfate and zinc sulfate. Eh values decreased with flooding time in both soils. The changes of Eh values were more negative in control treatments and stabilized after 3 weeks of submergence. The Eh variation was not observed effectively in the treated soils however, soil pH increased with flooding time. During the 3rd week of submergence, pH was neutral (pH 7.0). In both soils, Cu and Zn treated soil showed lower Eh and higher pH values as compared to untreated soil. Concentration of Cu and Zn in soil solution decreased with flooding. The higher Cu and Zn contents in soil were recorded in treated soils. Reduced solubility of Cu and Zn in control soils was related to larger changes in Eh and pH values. Mean comparison with Tukey’s HSD (Honest Significant Difference) test showed that Cu and Zn solubility decreased with decreased Eh and increased pH in the soil solution (p < 0.05%).

This paper proposes a mechanism for OELC (Optimizing Electricity Load and Cost) for smart grid. The load of every smart home is predicted one-hour prior to their actual usage. To fulfill PL (Predicted Load) of each consumer, multiple resources of electricity are considered, including RE (Renewable Energy) resources. Furthermore, cost to get PL from multiple resources is calculated. In proposed model 3-4 smart homes are grouped in the form of clusters. To reduce the amount of electricity bills, system also allows privileges to share electricity between adjacent smart homes within a cluster. To validate the OELC mechanism, extensive numerical simulations are conducted which shows a significant reduction in electricity load and cost for electricity consumers. In future, to enhance the functionality of OELC, security from cyber-attacks can be considered.

During the last decade, the CR (Cognitive Radio) came into view as a major wireless technology to resolve the issue of spectrum secrecy and efficient spectrum utilization. However, due to unlicensed (secondary) users, there are various security threats to the CRN (Cognitive Radio Networks). Some malicious users may access the CRN and mislead the secondary users to vacate the occupied channel, which may stop the communication. In this work, we propose a new cryptographic-based algorithm, CRAES (Cognitive Radio-Advanced Encryption Standard), inspired by the traditional AES to secure the CRN. The data of the primary and secondary users is encrypted at the transmitter and decrypted at the receiver. Unlike the conventional AES, we introduce the data-dependent key-generation and shift-rows process. We also reduce the rounds of AES from 10-6 to improve the computational efficiency without compromising the overall security. The experimental results demonstrate the effectiveness of the proposed CR-AES in terms of better security, reliability, and computational efficiency.

TL (Transmission Line) congestion is a key factor that affects the power system operational cost. In addition of renewable generation in National Grid of Pakistan, transmission line congestion are frequent. Consequently, the network in this particular region faces severe congestion and dynamic stability problems. It has been planned that renewable plants shaved to curtail some available generation to minimize this inevitable congestion. However, one of the cost-efficient solutions to this problem is series compensation of lines using TCSC (Thyristor Controlled Series Compensation). It significantly increases the transfer capability of existing power transmission and enhances the dynamic stability of system at a lower cost, and has shorter installation time as compared to the construction of new TLs. This paper deals with the dynamic modeling of a TCSC in the NTDC (National Transmission and Dispatch Company) network with its applications to alleviate congestion during fault conditions. This study has been carried out using simulation software PSS/E (Power System Simulator for Engineers) which does not have a predefined dynamic model for TCSC, this leads to the necessity of creating a user defined model. The model of TCSC has been programmed in FORTRAN and compiled along with existing dynamic models of network components. The results indicate that power flow and dynamic stability of network is enhanced.

The effluent water brought by RBOD (Right Bank Outfall Drain) is not only threat to the aquatic life of Manchar Lake but also the fertile agricultural lands which are being cultivated by use of lake water through Danastar Wah are at risk of salinization. The farming community of the area is scary of continual use of irrigation waters received through the Danastar Wah; they are of the view that the constant use of this water will ruin their fertile lands into salt-affected soils. Thus, keeping in view the fears of the farmers of the command area of Danastar Wah, a study was carried out to investigate the water quality of the Manchar Lake, RBOD MNV (Main Nara Valley) drain and Danastar Wah, and to examine soil salinity status of the area using Geo-referenced field and satellite imagery data for Kharif season of the year 2015. The results of the study showed that the EC (Electrical Conductivity) of the Danastar Wah water was below 1.2 dS/m. Thus, the water was suitable for irrigation purpose. In all the water samples, Na+, Ca2+ + Mg2+ and CO3 concentrations were found within the permissible limits, while no concentration of HCO3 was found in any of the water samples. In the command area, clay texture was dominant down to a depth of 60 cm soil profile. In the area about 37, 28, and 30% of the soils were normal (non-saline), saline and sodic, respectively; while only 5% of soils were saline-sodic. The cotton crop was identified as the major Kharif crop, occupying about 13.76% (2,844 ha) of the total command area, followed by rice crop grown on about 5.21% (1,078 ha) of the command area. The overall accuracy of image classification was 90% with a kappa coefficient of 0.86. Based on this study, it can be concluded that the water of the Danastar Wah can be used for irrigation purpose during Kharif season only with the condition that adequate land drainage is maintained. It is also suggested that before using the water of Manchar Lake, RBOD and Danastar Wah for Rabi season, analysis for water quality be conducted. GeoInformatics (GIS and RS) tools can be employed for spatial and temporal monitoring of water quality of the Manchar Lake.

We derive three-step algorithm based on divided difference to generate a class of 6-point n-ary interpolating sub-division schemes. In this technique second order divided differences have been calculated at specific position and used to insert new vertices. Interpolating sub-division schemes are more attractive than approximating schemes in computer aided geometric designs because of their interpolation property. Polynomial generation and polynomial reproduction are attractive properties of sub-division schemes. Shape preserving properties are also significant tool in sub-division schemes. Further, some significant properties of ternary and quaternary sub-division schemes have been elaborated such as continuity, degree of polynomial generation, polynomial reproduction and approximation order. Furthermore, shape preserving property that is monotonicity is also derived. Moreover, the visual performance of proposed schemes has also been demonstrated through several examples.

Collaborative effort is required in DSD (Distributed Software Development) in order to develop software in manageable components. Immense effort is taking place due to the cultural, geographical and temporal distances among project teams in Pakistani Software Industry to this end. The purpose of this study is to propose a solution during DSD that will help the distributed teams in coordinating their activities. Our solution enhances the management associated activities and assists in optimal usage of team coordination in dispersed settings. The method that we used is single case study type, which involved distributed software business to assess the usefulness and efficiency related to Pakistan’s DSD industries, and has shown strong tendency of participants towards the effectiveness of proposed team coordination framework. Results indicate that the predictable solution will look up the group management, which is considered as a vigorous issue of Pakistani DSD industry. Furthermore, it will assist in resolving the problems of team coordination in distributed situation. One of the most significant current discussion about the proposed solution suggested that it helps in human communication methods team coordination in a distributed environment.

Thermal conductivity displays a key role in design of engineering structures where, thermal stresses resulting from heat and temperatures are of concern. Significant efforts were made to measure the thermal conductivity of different materials. For thermal conductivity characterization of soil samples it is essential to have very flexible set-up. Hence, this paper provides details about indigenously developed experimental setup for thermal conductivity measurement. The design of this newly developed setup is based on the basic principle of steady state heat flow. This experimental setup is designed in order to measure the thermal conductivity of various materials such as soils, rocks, concrete and any type of unbonded and bonded materials. In this paper, initially the theoretical background of the measurement techniques and the principle of heat flow are described, followed by design description and working procedure. The design has been kept very simple, adjustable for varying type and size of specimens and easy to operate with excellent level of accuracy as evident from system calibration. The accuracy and precision of the newly developed setup was verified by testing reference materials of known thermal conductivity and in the test results a high correlation coefficient (R2 = 0.999) between experimental data and fitting curve was achieved.