Showing papers by "Chandigarh University published in 2016"

Modified Grey Wolf Optimizer for Global Engineering Optimization

Abstract: Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer GWO, which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO mGWO is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays

Abstract: This paper presents a novel binary algorithm named as binary spider monkey optimization (binSMO) for thinning of concentric circular antenna arrays (CCAA). The proposed algorithm has been adapted from a recently developed nature inspired optimization method, spider monkey optimization (SMO). SMO works in continuous domain and as such is not suitable for application to binary optimization problems. The binSMO algorithm has been proposed with inclusion of logical operators in SMO for binary thinning problem. Thinning of an antenna array reduces the maximum side lobe level (SLL) as well as cost and size of antenna array. Thinning of CCAA can be modelled as 0–1 binary integer optimization problem. The proposed binSMO is used to synthesize CCAA in order to reduce the SLL and at the same time keeping the percentage of thinning equal to or more than the desired level. Simulation examples of two ring and ten ring CCAA have been considered. The novel method binSMO gives reduced SLL as compared to the resul...

Structural, optical and magnetic properties of Gadolinium-doped ZnO nanoparticles

Abstract: Zn1−xGdxO (x = 0, 2, 5, 10, 15 %) nanoparticles were synthesised by sol–gel method and analysed for their structural, morphological, optical and magnetic properties. X-ray diffraction confirms formation of hexagonal wurtzite single phase of the synthesized nanoparticles. Transmission electron microscopy shows that they have spherical morphology; UV–Vis spectroscopy shows that they undergo red shift; energy dispersive technique reveals that they have the prerequisite elements in the stoichiometry ratio; and, vibrating sample magnetometer depicts that they have room temperature ferromagnetism, which, further, enhances upon Gd-doping.

Evolution of Recommender Systems from Ancient Times to Modern Era: A Survey

Abstract: Recommender systems were introduced in the mid-1990s to help people select the most suitable product for them from the plethora of options available with them. The idea that led to their development was that we people often rely on the opinions of our peers before trying something new, say it be before buying a smart phone, a laptop, before going for a movie, before going to a new restaurant and even before visiting a doctor. Till date, we have numerous recommender systems developed for various areas, using different recommendation approaches. Yet, there are still a few limitations of recommender systems that need to be worked on. In this paper, we present an overview of recommender systems, the various approaches of recommender systems, the application areas for which various recommender systems have been developed and we also present the limitations of recommender systems.

Comparative Analysis on Load Balancing Techniques in Cloud Computing

Abstract: Background/Objectives: Cloud computing is an arena that is ruling the world of information technology. Every user has its own definition for this technology as per their use. This paper is properly discussed document that describes the complete evolution of cloud computing from its beginning. Findings: With the presence of vast literature in field of load balancing, it was found confusion for the new scholars to find the startup point for their research in this field. Therefore, an exhaustive comparison has been made for the superior understanding of cloud evolution through various proposed algorithms from the past many decades, which will make the researchers possible to analyze the existing scenarios and a better way out to overcome the unsolved queries. Application/Improvements: The assessments between the algorithms will help the new researchers to analyze and opt for the parameters those need much more concentration to meet the required targets for better outcomes in the field.

Optimal Synthesis of Linear Antenna Arrays Using Modified Spider Monkey Optimization

Abstract: This paper presents a novel optimization technique named as modified spider monkey optimization (MSMO) for the synthesis of linear antenna array (LAA). The proposed method is inspired from a recently developed spider monkey optimization (SMO) swarm intelligent technique. The competitiveness of SMO has been already proved using numerical optimization functions. To improve the performance of SMO, a MSMO algorithm based on dual-search strategy is proposed in this paper. This approach generates a new solution using a search equation selected randomly from a candidate pool consisting of two search strategies. The performance of the proposed method is tested by applying it to find the optimal solutions for standard benchmark functions. Further, the capability and effectiveness is also proved by using it for practical optimization problem, i.e., synthesis of LAA for three different cases. Experimental results show that MSMO outperforms other popular algorithms like particle swarm optimization, cuckoo search, firefly algorithm, biogeography based optimization, differential evolution, tabu search and Taguchi method in terms of reduced side lobe level and faster convergence speed.

Multi-objective optimization of laser curve cutting of aluminium metal matrix composites using desirability function approach

Abstract: The machining of aluminium metal matrix composite (MMC) material reinforced with SiC particles has been identified as typical process with traditional machining process. In the present study, curve cutting has been performed on the MMC material using CO2 laser cutting system. The investigations have been carried out to identify the thermal effects of SiC particles on surface roughness and kerf deviation. To optimize the quality characteristics, the response surface model is formulated using desirability functional approach. The optimal settings of various input variables have been identified for quality characteristics. The input parameters like cutting speed, reinforced particles and arc radius have been examined as significant factors. The adequacy of predicted response surface model was verified by performing the set of experiments and predicted error percentage method. The formation of recast layer and new phase Al4C3 was detected using SEM and XRD plot, respectively.

Influence of Ho3+ doping on the temperature sensing behavior of Er3+–Yb3+ doped La2CaZnO5 phosphor

Abstract: In this paper, a series of Er3+/Yb3+ and Er3+/Yb3+/Ho3+ codoped La2CaZnO5 (LCZ) upconversion (UC) phosphors were synthesized by the combustion route. The UC emission from LCZ phosphors, codoped with fixed Er3+, Ho3+, and various Yb3+ concentrations has been investigated. The structural and upconversion properties of the synthesized phosphors were studied in detail. Under 980 nm laser excitation, the codoped samples showed green UC emission that consisted of three well-known emission bands centered at 522, 548 and 672 nm generated by the 2H11/2 → 4I15/2, 4S3/2 → 4I15/2 and 4F9/2 → 4I15/2 transitions of Er3+ ions, respectively. The emission intensities of these bands have been enhanced sufficiently on codoping of Yb3+ ions in the LCZ : Er3+ system. An effort has been presented to explain the enhancement on the basis of a power dependence study and an energy level diagram. The luminescence lifetimes of the green emission of the LCZ samples with different codoping were also recorded and incorporated to explain the energy transfer mechanism. The strong temperature dependence of the fluorescent intensity ratio between two green emissions makes the material suitable for temperature sensing purposes and it is also suitable up to high temperatures of 500 K. A relatively high-temperature sensor with good sensitivity 0.00625 K−1 was found from the observed results. An increment of 6% for the sensitivity is observed over the existing LCZ phosphor after co-doping with Ho3+. These results indicate that the Er3+/Ho3+/Yb3+ codoped LCZ material is an effective UC phosphor and may be a potential candidate for high-temperature sensors.

Web News Mining using Back Propagation Neural Network and Clustering using K-Means Algorithm in Big Data

Abstract: In today’s era, technology increasing day by day that changes the needs and minds of human beings. In this modern world handling data and information is the main problem. Many new technologies are developed for gathering the large data like Big data. In Big data many techniques and technologies are there like K-means. In this paper the main focus of research is to manage huge amount of news like BBC news channel data from the internet. To overcome the problem of managing the web news data, a clustering based K-means and Back Propagation Neural Network algorithm for classification is proposed. In this news are classified and categorized on the basis of text and content to provide the accurate news with less running time.

Bio Concrete- The Self-Healing Concrete

Abstract: Background/Objectives: In the present scenario the use of bio concrete proved very fruitful for construction of durable structures and it also improved the properties of concrete and maintenance cost is reduced. Method/Statistical Analysis: In this paper an attempt has been made to use different microorganisms so as to observe the strength gain as a result of sprouting of filler material inside the cement sand matrix pores. The strength of concrete mix is evaluated by conducting compressive strength test, split tensile strength test and flexural strength test. Scanning Electron Microscopy(SEM)/XRay Diffraction(XRD) analysis is further done to prove involvement of the isolated ureolytic bacteria in calcium carbonate precipitation.Findings: From the tests conducted on various specimens of bio concrete using different bacteria, a clear comparison can be made for strengths of different specimens of bio concrete using different bacteria and convention concrete. The results obtained from the experiment concludes that when water enter in the concrete after the formation of cracks activates the dormant bacteria (inactive) by the process of metabolically mediated calcium carbonate precipitation and it increased the strength of bio concrete as compared to the conventional concrete.Applications/Improvements: Bio concrete is durable and offers better resistance to corrosion and freeze thaw. Crack remediation using bio-concrete is better than epoxy treatments.

Efficient clustering based OLSR routing protocol for VANET

Abstract: This paper proposed a Clustering approach based routing protocol for VANETs. The proposed algorithm is a distributed clustering algorithm together with OLSR Routing protocol, which possesses excellent Data dissemination rate, where Data dissemination is defined by throughput of protocol. In addition, the algorithm is also perform excellent in terms of End to End delay and exhibits a reasonable overhead. The algorithm is achieved by utilizing a new clustering technique based protocol called OLSR-C (OC). The clustering scheme uses a vehicle's position (provided by GPS) and velocity information to form clusters with low relative mobility between the cluster heads and their cluster members. The performance of the proposed algorithm is validated against the AODV, DYMO and OLSR routing protocol and outperform the routing protocol in term of End to End delay, throughput and PDR, thus provides a good benchmark for the algorithm's success.

Classification of mammogram images by using CNN classifier

Abstract: Classification of the breast tissues into the benign and malignant classes is a difficult assignment. The experimental results are takes 40 input images from DDSM dataset. We extract the GLCM, GLDM and Geometrical features from the mammogram images. In this paper we apply Convolution Neural Network as a classifier on the mammogram images to enhance the accuracy rate of CAD. Performance of the different classifiers is measured on receiver operating characteristic. In training stage, overall classification accuracy of 73%, with 71.5% sensitivity and 73.5% specificity for dense tissue is achieved by our proposed method along with it, accuracy of 79.23%, 73.25% sensitivity and 74.5% specificity is achieved for fatty tissue. Convolution neural network classifier is used to boost the classification performance. This classifier performs better than previous classifiers in that it shows more accuracy than the other classifiers, the misclassification rate of normal mammograms as abnormal. This approach performs good on overlapping problem. This method is different from all other approaches, which are used to identify normal mammograms by detecting cancers. Overlapped tissues are also detected by this using this classifier.

Comparative Analysis of Information Extraction Techniques for Data Mining

Abstract: Background/Objectives: This paper emphasizes the evolution of data processing adroitness to advanced data processing taxonomy from Mesolithic to recent years and a comparative study of prevailing tools/techniques which are useful for mainly the analysis of the bulky data. Methods/Statistical Analysis: There are various kinds of methods adapted by researchers for analysis of large amount of data. Each method varies on the basis of their different parameters and datasets according to their needs. These methods are implemented on HDFS, Mapreduce and Hadoop environment with integration of R tool. Some Methods are enhanced by the sentimental analysis through NLP which increase the performance of density analysis. Findings: The data or associated facts have been in existence right with the birth of human species. It commenced with manual illustration and gradually advanced through current state-of the art storage and processing. Big data involves novel techniques to manage information within limited run time. Big data is acutely beneficial in ventures growth, society incumbency and scientific research. The paper provides an overview of state of the art and focuses on the usage of conventional tools as well as advanced tools and techniques for effective information extraction. Applications/Improvements: To handle this prodigious data, there is a need to upgrade from the traditional data filtering techniques and adopt the new big data diagnostic tools.

A novel prime numbers based hashing technique for minimizing collisions

Abstract: Searching is a prime operation in computer science and numerous methods has been devised to make it efficient. Hashing is one such searching technique with objective of limiting the searching complexity to O (1) i.e. finding the desired item in one attempt. But achieving complexity of O (1) is quite difficult or usually not possible. This happens because there is no perfect mapping function for insertion and searching; and this imperfection of hashing function results in collisions. The algorithm and technique presented in this article minimizes the number of collisions by removing the problem of clustering. Clustering occurs when the data items congregates in one particular area thus increasing the number of collisions and results in increased number of probes to insert and search an item. During trials runs the proposed algorithm have shown considerable improvements over all major hashing algorithms in terms of performance.

Analyzing fragment production in mass-asymmetric reactions as a function of density dependent part of symmetry energy

Abstract: We performed a theoretical investigation of different mass-asymmetric reactions to access the direct impact of the density-dependent part of symmetry energy on multifragmentation. The simulations are performed for a specific set of reactions having same system mass and N/Z content, using isospin-dependent quantum molecular dynamics model to estimate the quantitative dependence of fragment production on themass-asymmetry factor (τ) for various symmetry energy forms. The dynamics associated with different mass-asymmetric reactions is explored and the direct role of symmetry energy is checked. Also a comparison with the experimental data (asymmetric reaction) is presented for a different equation of states (symmetry energy forms).

Cardio Vascular Disease Classification Ensemble Optimization using Genetic Algorithm and Neural Network

Abstract: Objective: To develop a new technique for the detection of heart disease and to build the detection system based on fuzzy logic algorithm for extraction of features by applying neural network classifier of heart disease. Methods/Statistical analysis: The disease dataset is classified by using Fuzzy logic, genetic algorithm and training is done by neural network by the extracting features. The image is tested on the basis of features of dataset and the extracted images. Findings: The accuracy is improved up to 99.97%. The error rate is reduced, it is .987 %. Application/Improvements: This paper presented the ECG signal modeling along with classification of diseases using fuzzy logic, Genetic Algorithm and Neural Network with improved accuracy and less error rate.

Experimental Studies on Heat Transfer Performance of Double Pipe Heat Exchanger with using Baffles and Nanofluids

Abstract: Objective: The present study was made to investigate the enhancement in the heat transfer characteristics of CuO water base nanofluids by inserting the baffles in the annulus of the double pipe heat exchanger. Statistical Analysis: The experimentation was carried out with distilled water and copper oxide water base nanofluids at 0.1% and 0.2% volume concentration. The effect on heat transfer coefficient and Nusselt number is determined in this experiment at various temperatures. Findings: It has been observed that in comparison to the distilled water, copper oxide nanofluids with baffles shows more heat transfer enhancement. The result showed that the Nusselt number increased by 8% without introducing baffles and nanofluids and 10-12% enhancement was observed with baffles and nanofluids. The enhancement of 22-25 % has been observed in the heat transfer coefficient at 0.1% volume concentration and 25-30 % of enhancement 0.2% volume concentration. Applications: In automobile radiators, cooling towers, air preheaters, refrigeration, air conditioning, condensers etc double pipe heat exchanger with baffles inserts can be easily implemented.

Synthesis and Sedimentation Analysis of Magneto Rheological Fluids

Abstract: Objectives: The aim of this work is that how the MR fluids can be prepared. Methods/Statistical Analysis: This paper highlights on the recent development of MR fluids. In this paper the preparation of MR fluid are described. Base fluid is taken as silicone oil and micron sized iron powder is mixed in it. To reduce the sedimentation White lithium grease is also mixed as an additive in the prepared fluid. Finding: The sedimentation analysis is important aspect in case of MR fluids. The composition of MR fluid samples is taken from the base paper. For the preparation of MR fluids three components are used. These three components are base fluid, metal particles, additives. With different composition of three components three MR fluid samples are prepared. The sedimentation properties are studied by visual inspection. The effects of temperature on the viscosity of the prepared fluid samples are investigated. The viscosity of the MR fluid plays an important role in its working. The viscosity of the MR fluid depends on its composition, more the iron content the viscosity will be higher. At different value of temperatures the value of viscosity is calculated. The effects of temperature on viscosity are observed. Application/Improvements: An electrical stirrer is used for the preparation of the MR Fluids. Magneto rheological fluids are the smart materials whose properties change rapidly and can be controlled easily in the presence of an applied magnetic field.

Experimental Analysis of Heat Transfer and Friction Factor in Plate Heat Exchanger with Different Orientations Using Al2O3 Nanofluids

Abstract: Experimental investigations has been done to find out the heat transfer characteristics and friction factor of water based Al2O3 nanofluids as a coolant in brazed plate heat exchanger. Plate heat exchanger either use horizontally or vertically. The base plate of the plate heat exchanger was kept inclined at (0⁰, 30⁰ ,60⁰, 90⁰). The experimentation have been done on the two different concentration of the nanofluids (0.1 v/v% and 0.2 v/v%). It was observed that the heat trasnfer characterstics improves with increase in Reynolds number. It has been studied that nanofluids in a plate heat exchanger has maximum of 34% heat transfer rate over the base fluid. It has been observed that from horizontal to vertical heat transfer rate decreases with increase in Reynolds number. Results shows that there is (10-15) % has been found out when the base plate of plate heat exchanger made inclined from horizontal to 30⁰ of inclination.

A real time speech to text conversion system using bidirectional Kalman filter in Matlab

Abstract: A real time speech to text conversion system converts the spoken words into text form exactly in the similar way that the user pronounces. We created a real time speech recognition system that was tested in real time noiseous environment. We used the design of a bidirectional nonstationary Kalman filter to enhance the ability of this Real time speech recognition system. Bidirectional Kalman filter has been proved to be the best noise estimator in nonstationary noiseous environment. Real time speech to text conversion system introduces conversion of the uttered words instantly after the utterance. The purpose of this project was to introduce a new speech recognition system that is computationally simple and more robust to noise than the HMM based speech recognition system. We have used our own created database for its flexibility and TIDIGIT database for its accuracy comparison with the HMM based speech recognition system. MFCC features of speech sample were calculated and words were distinguished according to the feature matching of each sampled word. System was tested in different noise conditions and we obtained overall word accuracy of 90%.

Hybrid Image Fusion Algorithm Using Laplacian Pyramid and PCA Method

Abstract: Image fusion is a procedure of merging compatible information from two or more images in to one image. Such fused image will be more descriptive in nature or have sufficient information than the source images. One of the fusion techniques like DWT contains shift variant issues in which it does not preserve edges of objects in fused images. To preserve the edges, smoothening and other important issues has been resolved in this paper using combination of Laplacian Pyramid and PCA techniques. Different level of an input image are created using Laplacian Pyramid Method and further the top level are fused using PCA algorithm. Resultant fused image is again reconstructed by the pyramid method. The parameters considered for the evaluating quality of fused image are Bit Error rate, MSE and PSNR. The results are being compared with DWT techniques.

A review to detect leukemia cancer in medical images

Abstract: Health informatics has been qualified as prominent province in the headway of information technology. Ascribable to such a sophisticated evolution in the health care informatics, it is viable at the present period of time to diagnose several ailments in a short span of time. In relation to complaints, there is one disease dub leukemia which can be recognised by manipulating different techniques of information technology. Leukemia customarily occurs when a big portion of nonstandard White Blood Cells produced in the body by bone-marrow. Hematologist makes usage of microscopic study of human blood-cells which leads towards the requirement of several different methods that consist of microscopic-images, segmentation process, grouping as well as classification that can allow proper identification of numerous distinct patients that are having leukemia disease. The image data-set of microscopic ridges would be inspected visually by using some hematologists as well as this process is quite time consuming along with exhausting. The well-timed and fast discovery of leukemia considerably aids in providingaptcure to the sick-patient. The necessity for computerization of detection of this disease generally rises perpetually since modern techniques that include proper manual-investigation of the tissues of the blood as primary step in the direction of disease diagnosis. This procedure is relatively time-consuming, along with their proper accurateness depend upon the proficiency of operator's. So, prevention of leukemia is quite important. This paper has surveyed several methods utilized by prior authors such as ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.

Wearable antenna for skin cancer detection

Abstract: This paper presents a high gain wearable metamaterial antenna for biomedical applications. The wide band antenna is designed such that it mimics the electrical properties of the human skin. The proposed antenna operating in the X-band (8–12 GHz) allows sensing of varying differences in dielectric properties among skin tissues over a wide band of frequency and thus, can be used for skin cancer detection. The proposed antenna resonates at 11.72GHz with gain of 10.9dBi. This wideband antenna is designed and simulated using EM wave solver i.e. High Frequency Structured Simulator (HFSS). This structure showed that symmetrical slotting along y axis modifies bandwidth as well as improves the gain. Here, metamaterials are used to achieve size reduction and highly directive property.

BotMAD: Botnet malicious activity detector based on DNS traffic analysis

Abstract: Botnet is a collection of infected computers i.e. collection of zombie PCs which are remotely controlled by a single person or a group so called botmaster. In the recent years, botnets are becoming stealthier in nature by implanting certain techniques to hide themselves such as fast flux or DGA algorithms to generate the domain names. Generally, the class of botnet can be categorized into two major class-one which exploiting the IP protocol and another is using the DNS protocol for communications. The bot malwares who are using the DNS protocol are designed to remain unaffected over a long period of time. Once they receive the commands from the botmaster, they start to response to execute further actionable commands to perform SPAMs or DDoS attacks. To address such issues, BotMAD-Botnet Malicious Activity Detection based on DNS traffic pattern analysis is presented to detect such class of botnet family which are not detected by IP protocol based exploiting technique because IP may be changed by the botmaster by using fast flux or other techniques to make them stealth in nature. BotMAD — an automated DNS traffic Analyzer and Detector is introduced which automatically detect the malicious IP/Domain pair by inspecting the DNS packets from the network traces. Further the feed of DNSBL database is integrated with the system by fetching the records of malicious domains through Intel critical-stack API to enrich the database. To validate the accuracy of the system, two data sets are used-one is network traces of bot malwares captured on honeypots and second one domain reputation engines for validation. In the end, we conclude that the developed framework is giving the promising results in the form of botnet domain detection.

Power System Stability for the Islanding Operation of Micro Grids

Abstract: Objectives: A different power sources has been used and integrated in past keeping an objective for futuristic control over demand and integration to provide smart Grid. Methods/Statistical Analysis: New Hybrid model is proposed to enhance performance of micro grid. The method involves a controller with load with simple design procedure with numerical terminology. Considering PV panels, various functions express the irradiation at specified operational temperature like VMP; IMP where in order to reach MPPT value, traditional P&O methods require a few perturbation steps. Finding: In addition, Implementation is also done using Pulse Width modulation in converters while switching for utilizing total voltage in a loop. This technique also improves the voltage regulation with less value of THD. The design will be made on SIMULINK of MATLAB and algorithm codes will be written in editor of MATLAB. The result of hybrid model was found to be quite better than the incremental conductance technique in terms of output voltage magnitude and THD content. Application/Improvements: This paper aims at developing a novel technique based on Meta-heuristic approach for improved performance of MPPT method for solar cells connected to a grid.

Emotional Intelligence and Stress Coping Styles: A Study of Doctors of Private Hospitals in and Around Chandigarh

Abstract: Emotional Intelligence, or EI, describes an ability or capacity to perceive, assess, and manage the emotions of one’s self, and of others. Goleman describes emotional intelligence as "managing feelings so that they are expressed appropriately and effectively, enabling people to work together smoothly toward their common goals." According to Goleman, the four major skills that make up emotional intelligence are: Self-Awareness, Self-Management, Social Awareness and Relationship Management. Stress is a fact of life and is inevitable in this competitive world. From individual point of view stress is our body’s physical, chemical and mental reaction to circumstances that frighten, confuse, endanger or irritate us. It can lead to physiological discomfort, some kind of emotional unhappiness , and even s trained relationship with others. EI has been linked with various stress coping strategies particularly rational/problem focused coping. The link between EI and stress is also established in the academic literature. Stress Coping strategies refer to the specific efforts, both behavioral and psychological, that people employ to master, tolerate, reduce, or minimize stressful events. Proposed study is an attempt to find out the relationship between emotional intelligence and stress coping styles among doctors of private hospitals in and around Chandigarh.