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Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.

The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction.

Plant nucleotide-binding leucine-rich repeat immune receptors (NLRs) directly or indirectly recognize pathogen-secreted effector molecules to initiate plant defense. Recognition of multiple pathogens by a single NLR is rare and usually occurs via monitoring for changes to host proteins; few characterized NLRs have been shown to recognize multiple effectors. The barley NLRMlahas undergone functional diversification andMlaalleles recognize host-adapted isolates of barley powdery mildew (Blumeria graminisf. sp.hordei; Bgh). Here, we show thatMla3also confers resistance to rice blast (Magnaporthe oryzae) in a dosage dependent manner. Using a forward genetic screen, we discovered that the recognized effector fromM. oryzaeisPWL2, a host range determinant factor that preventsM. oryzaefrom infecting weeping lovegrass (Eragrostis curvula).Mla3has therefore convergently evolved the capacity to recognize effectors from diverse pathogens.

Background: In this astronomically immense world tremendous amount of data engendering in every minute from the different domain which is referred to Big Data. In the last few years the data is incrementing day by day across the world. This Research fixates on the analysis of malefaction rates of 5 different states year wise, all the analysis is done utilizing Apache Pig. Methods: The goal of the work is to analyze the astronomically immense malefaction data and find the estimate number of malefaction transpires in sundry states. This is done in Apache pig environment utilizing “Pig Latin” as language. A short code is indicted in Pig Latin which is utilized to load and process the data into Map reduce environment, afterwards the result are obtained with the detail of minimum and maximum mapper and reducer timing. Results: The data is visualized into graphs to make analysis to analyze the variation of malefaction rates in distinct states. After analyzing the malefaction against women, murder cases are very high in 2006-2010 as compared to other year groups whereas abducting and rape cases incremented perpetually from 2001 to 2014 respectively. Similarly all the reports regarding to different malefaction rates are visualized above by utilizing graphs. Conclusion: Various results are found with sundry queries and everything is represented graphically for better understanding and comparison. This avails us to find which state is affected by which crime. The expeditiousness of Apache pig can additionally be optically discerned as this immensely colossal crime data processed in short time with precision.

COVID19 is a contagious ailment, which is emanated from the city of Wuhan (Hebei district), China in late of 2019, genesis from newly come across corona virus. Humans are contaminated with the COVI...

The inflammatory bowel disease (IBD)-disk is a validated, visual, 10-item, self-administered questionnaire used to evaluate IBD-related disability. The present study aimed to evaluate IBD-disk in assessment of IBD daily life burden and its relation with disease activity.A cross-sectional study was conducted between June 2021 and December 2021. Patients with IBD were asked to complete the IBD-disk and a visual analogue scale of IBD daily-life burden (scored from 0-10, score5 indicative of high burden). The internal consistency of IBD-disk, correlation with IBD daily life burden and disease activity (assessed by partial Mayo score and Harvey Bradshaw Index in patients with ulcerative colitis [UC] and Crohn's disease [CD], respectively) and diagnostic performance of IBD-disk to detect high burden were analyzed.Out of the 546 patients (mean age 40.33±13.74 years, 282 [51.6%] males) who completed the IBD-disk, 464 (84.98%) had UC and the remaining (n=82, 15.02%) had CD. A total of 311 patients (291 UC and 20 CD; 56.95%) had active disease. The mean IBD-disk total score and IBD daily life burden were 18.39±15.23 and 2.45±2.02, respectively. The IBD-disk total score correlated strongly with the IBD daily life burden (ρ=0.94, P0.001), moderately with partial Mayo score (ρ=0.50) and weakly with Harvey Bradshaw Index (ρ=0.34). The IBD-disk total score30 predicted high IBD daily-life burden.The IBD-disk accurately predicts the daily life burden and parallels disease activity in patients with IBD and can be applied in clinical practice. (Intest Res, Published online ).

Background/Aims: Inflammatory bowel disease (IBD) is increasingly being recognized in elderly patients. Data on clinical spectrum of elderly-onset IBD patients is lacking from India.Methods: A cross-sectional retrospective analysis of a prospectively maintained database of patients diagnosed with IBD was conducted at 2 centers in India. The clinical spectrum of elderly-onset IBD including demographic profile (age and sex), clinical presentation, disease characteristics (disease behavior and severity, extent of disease), and treatment were recorded and compared with adult-onset IBD.Results: During the study period, 3,922 (3,172 ulcerative colitis [UC] and 750 Crohn’s disease [CD]) patients with IBD were recorded in the database. A total of 186 patients (4.74%; 116 males [62.36%]) had elderly-onset IBD (69.35% UC and 30.64% CD). Diarrhea, blood in stools, nocturnal frequency and pain abdomen were the commonest presentations for UC, whereas pain abdomen, weight loss and diarrhea were the most frequent symptoms in CD. For both elderly onset UC and CD, majority of the patients had moderately severe disease. Left-sided colitis was the commonest disease location in UC. Isolated ileal disease and inflammatory behavior were the most common disease location and behavior, respectively in CD. 5-Aminosalicylates were the commonest prescribed drug for both elderly onset UC and CD. Thiopurines and biologics were used infrequently. Prevalence of colorectal cancer was higher in elderly onset IBD.Conclusions: Elderly onset IBD is not uncommon in India. Both the elderly onset UC and CD were milder, with no significant differences in disease characteristics (disease extent, location and behavior) when compared to adult-onset IBD. Colorectal cancer was more common in elderly onset IBD.

Cloud computing is a term for a wide range of developments possibilities. It is rapidly growing paradigm in software technology that offers different services. Cloud computing has come of age, since Amazon's rollouted the first of its kind of cloud services in 2006. It stores the tremendous amount of data that are being processed every day. Cloud computing is a reliable computing base for data-intensive jobs. Cloud computing provide computing resources as a service. It is on-demand availability of computing resources without direct interaction of user. A major focus area of cloud computing is task scheduling. Task scheduling is one among the many important issues to be dealt with. It means to optimize overall system capabilities and to allocate the right resources. Task scheduling referred to NP-hard problem. The proposed algorithm is Cost Effective ACO for task scheduling, which calculates execution cost of CPU, bandwidth, memory etc. The suggested algorithm is compared with CloudSim with the presented Basic Cost ACO algorithm-based task scheduling method and outcomes clearly shows that the CEACO based task scheduling method clearly outperforms the others techniques which are in use into considerations. The task is allotted to the number of VMs based on the priorities (highest to lowest) given by user. The simulation consequences demonstrate that the suggested scheduling algorithm performs faster than previous Ant Colony Optimization algorithm in reference to the cost. It reduces the overall cost as compare to existing algorithm.

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.

Background:Candida auris has emerged globally as a multi-drug resistant yeast and is commonly associated with nosocomial outbreaks in ICUs. Methods: We conducted a retrospective observational multicentre study to determine the epidemiology of C. auris infections, its management strategies, patient outcomes, and infection prevention and control practices across 10 centres from five countries. Results: Significant risk factors for C. auris infection include the age group of 61–70 years (39%), recent history of ICU admission (63%), diabetes (63%), renal failure (52%), presence of CVC (91%) and previous history of antibiotic treatment (96%). C. auris was commonly isolated from blood (76%). Echinocandins were the most sensitive drugs. Most common antifungals used for treatment were caspofungin (40%), anidulafungin (28%) and micafungin (15%). The median duration of treatment was 20 days. Source removal was conductedin 74% patients. All-cause crude mortality rate after 30 days was 37%. Antifungal therapy was associated with a reduction in mortality (OR:0.27) and so was source removal (OR:0.74). Contact isolation precautions were followed in 87% patients. Conclusions:C. auris infection carries a high risk for associated mortality. The organism is mainly resistant to most azoles and even amphotericin-B. Targeted antifungal therapy, mainly an echinocandin, and source control are the prominent therapeutic approaches.

Big data analytics is becoming tremendously popular in every field today. Everyday lots of data are being generated and analyzed using big data analytics tools and technique. Here the technology used is apache spark and language used is Scala. So, in this paper study is being done on the behalf of research done in stock market data using apache spark technique. Here the nifty-50 data is taken to analyze the impact due to covid-19. As it is being seen that Covid-19 has affected almost everything around the globe, so the purpose is to analyze its effect on stock market. Thereafter comparison is done between the techniques used to analyze that massive volume of stock exchange data. Here the comparative analysis between Hadoop maps-reduce and apache spark on the behalf of some important parameter is being done. That concludes which technique is better for the analysis of the stock exchange data.

Data is generated by humans every day via various sources such as Instagram, Facebook, Twitter, Google, etc at a rate of 2.5 quintillion bytes with high volume, high speed and high variety. When this huge volume of data with high velocity is handled by the traditional approaches, it becomes inefficient and time-consuming. Apache Spark technology has been used that is an open-source in-memory clusters computing system for fast processing. This paper introduces a brief study of Big Data Analytics and Apache Spark which consists of characteristics (7V’s) of big data, tools and application areas for big data analytics, as well as Apache Spark Ecosystem including components, libraries and cluster managers that is deployment modes of Apache spark. Furthermore, this also presents a comparative study of Python and Scala programming languages with many parameters in the aspect of Apache Spark. This comparative study will help to identify which programming language like Python and Scala are suitable for Apache Spark technology. As result, both Python and Scala programming languages are suitable for Apache Spark, however language choice for programming in Apache Spark is depending on the features that best suited the needs of the project since each one has its own advantages and disadvantages. The main purpose of this paper is to make things easier for the programmer in the selection of programming languages in Apache Spark-based on their project.

Big data analytics is the one which acquire, organise and analyse the huge volume of data with high velocity to find some patterns and useful information. The data sets are so large that it can’t be handled by traditional databases to manage and process the structure and unstructured data. Hence, big data tools i.e. Hadoop, is required due to its high scalability, availability and cluster environment mechanism for analysing large volume of data. MapReduce is one of the important components of Hadoop which is able to handle the unstructured data. But to use MapReduce, high programming skills are needed. Therefore, due to the reason of programming, users are moving towards some other tools i.e. Apache Pig or Apache Cassandra. In these tools, the data is simply analysed by executing the queries or commands. This paper will discuss about the architectural of Apache Pig and Apache Cassandra and afterwards both the technologies regarding some factors are compared to find out which one is better.

Coronavirus disease (COVID-19) in children largely causes mild disease. Some of these recovered children may present with delayed manifestations described as Hyperinflammatory syndrome resembling closely with Kawasaki disease (KD) or Toxic Shock Syndrome (TSS). Timely identification, supportive treatment and treatment with immunomodulatory drugs make a significant difference in the outcome.

1-Aminobenzotriazole (ABT) is a mechanism-based inactivator of major cytochrome P450 (CYP) enzymes, which is used in multiple mechanistic studies. The purpose was to evaluate the effect of 2 and 16-h pretreatment regimens of ABT on the exposures of triazolam in rat. Another objective was to evaluate the effect of ABT on gastric emptying of acetaminophen. Plasma area under the curve (AUC) of triazolam was increased by 101-fold and 81-fold for the rats pretreated with ABT at 2 and 16 h, respectively, compared to control rats. Time to reach maximum concentration was 0.3, 4.8 and 3.7 h in control, 2 and 16-h pretreatment animals, respectively. In the case of acetaminophen, where Tmax was not delayed, the mean absorption time (MAT) in control, 2 and 16 h ABT pretreatment groups were 0.3, 4.6 and 2.9 h, respectively, suggesting delayed absorption. This hypothesis was further supported by GastroPlusTM simulation. In summary, extent of triazolam absorption was increased to a similar extent with both 2 and 16 h ABT pretreatment regimens, suggesting that either of the regimen can be used to increase parent exposures in rat. With ABT pretreatment, delayed absorption of triazolam and acetaminophen was observed, as suggested by delay in Tmax and MAT, respectively.

Even today, news channels in India remain credible to the public. Through this research, the style of presentation of news channels is revealed. Which news channel is at the forefront of the fierce competition between news channels, has also been explored. This research can play an effective role in the prevention of ignorance of aspects related to television media. Today, television is the most effective medium of communication. Television media can help people in getting justice, removing the veil from various untouched aspects. At the same time, there have been some limitations of this research, like - only Jaipur city has been selected for this research. So that the quality of work remains. In this research only four Hindi news channels have been selected for study. In this, only voters have been taken from the electoral rolls of Jaipur city and only 1000 respondents have been taken for the study. Questionnaire is the main means of collecting data in this research. At the same time, the schedule is a method of collecting such facts in which observation, interview and questionnaire are the co-ordinates of the three features. Through interviews, data has been compiled in two ways, one from people connected to TV news channels and the other to students of media. भारत में आज भी समाचार चैनलों की जनता मंे विश्वसनीयता बनी हुई है। इस शोध के माध्यम से समाचार चैनलों के प्रस्तुतिकरण के अंदाज का पता चलता है। समाचार चैनलों के बीच चल रही घमासान प्रतिस्पर्धा में सबसे आगे कौनसा समाचार चैनल है, का भी पता किया गया है। यह शोध टेलीविजन मीडिया से संबंधित पहलुओं की अज्ञानता के निवारण में प्रभावी भूमिका निभा सकता है। आज टेलीविजन ही संचार का सबसे प्रभावी माध्यम है। टेलीविजन मीडिया लोगों को न्याय दिलाने में, विभिन्न अनछुए पहलुओं से पर्दा हटाने में सहायक सिद्ध हो सकता है। वहीं इस शोध की कुछ सीमाएं भी रही हैं जैसे- इस शोध में केवल जयपुर शहर को ही अध्ययन के लिए चुना गया है। जिससे कार्य की गुणवत्ता बनी रहे। इस शोध में केवल चार हिन्दी के समाचार चैनलों को अध्ययन के लिए चुना गया है। इसमें जयपुर शहर की निर्वाचक नामावली में से मतदाताओं को ही लिया गया है तथा अध्ययन के लिए केवल 1000 रेस्पोंडेंट्स को ही लिया गया है। इस शोध में आंकडे एकत्र करने का मुख्य साधन प्रश्नावली ही है। वहीं अनुसूची एक ऐसी तथ्य संकलन की प्रविधि है जिसमें अवलोकन, साक्षात्कार तथा प्रश्नावली तीनों विशेषताओं का समन्वय है। साक्षात्कार के माध्यम से दो तरह से आंकडों का संकलन किया हैं जिनमें एक टीवी के समाचार चैनलों से जुड़े लोगों का एवं दूसरा मीडिया के विद्यार्थियों से।

Data is stored by the individuals, small or big organizations, industries and governments for a longer duration to fulfill different objectives. Huge store of data which is growing speedily has motivated an emergent need for the development of automated tools in order to efficiently organize, examine and discover hidden information from the collected data. It is the initial stage where data is to be collected from heterogeneous sources and all are stored in one place. It is required to maintain the data, build information and generate knowledge from it. Finally, distribute the retrieved information and knowledge to the end user for their interpretation. An immense use of computers and electronic devices has resulted in explosive growth in data collection. To analyze and produce the productive conclusion from this large amount of data, the special tool called data mining is required. Thus, the huge information that is buried under voluminous database has given birth to data mining technique. Data mining is not a very old technology. Challenges were found in conventional data analysis techniques to handle large as well as new types of datasets. Thus, researchers focused on developing proficient and more scalable tools to handle diverse data easily.

Big data refers to the accumulation of sundry kinds of data in a plethora. In healthcare science, it refers to some radiologistic medical images datasets. This paper converses about medical imaging modalities such as CT scan, MRI, USG, and X-ray images to extract the hidden information in the form of pixels contained in these images. Pixels are the most prominent addressable elements in an image, and are quantified in dpi. The number of pixels in an image refers to the amount of information in that particular image. Analysis of medical images is very paramount aspect as they have so much consequential information, and leaving that information hidden and undetected can cause quandaries in near future. In medical science, radiologists face many quandaries as the images have very less number of pixels and the information is not feasibly extractable. With the avail of some implements and technologies used to analyze immensely colossal data, we can extract this hidden information from medical images and count the number of pixels of several images at the same period of time. In this article, we will perform pixel count on medical images in distributed environment. There are sundry implements for this purport, but here we have utilized Hadoop Image Processing Interface (HIPI) to perform this task in expeditious speed utilizing Hadoop Distributed File System (HDFS) and clusters of commodity hardware.

in the current era IT enabled world, term "Big Data" is a new concept used to define the collection of data set which is large and complex. There are different sources defined which generates huge amount of data on daily basis like social media, stock exchange, medical science or clinical healthcare data which are elaborated in distributed fashion around the world. In this paper change on data by size may require new tools and storage methods to overcome the problem of storing and processing big data. Hadoop is an open source framework to analyze massive volume of data. One of the precious components of Hadoop is Apache Pig. Pig processes the data in the form of Latin script. We attempt to inspect stock exchange data of banking sector using Pig. In this observation, we worked on yearly data to come across 52 week low, high, average data and perceive the mapper and reducer time with the help of pig latin script for the banking data. All the observed result presented in the graphical form. In the research, we considered data of 6 years of different banks such as AXIS, HDFC, ICICI, and SBI

We have investigated the problem of underwater hazy image enhancement and restoration in this paper studied. Underwater image processing has several applications in the field of oceanic research work and scientific applications such as archaeology, geology, underwater environmental assessment, laying of long distance gas pipelines and communication links across the continents which demand geo-referential surveying of the oceanic bed and prospection of ancient shipwreck. There are many difficulties for undersea optical imaging. To submerging a camera in underwater enough space is required. The maneuvering of the camera with the help from remote place or in person at the site is likewise a complex task. However, the major challenge is imposed by underwater medium properties. Underwater haze image enhancement has gained widespread importance with the rapid development of modern imaging equipment. Though, the contrast enhancement of single underwater hazy image is a cumbersome task for scientific exploration and computational application. At extreme depth, because of attenuation in light propagation, the underwater images are susceptible to inferior visibility.

Medical data are increasing continuously so we need to classify these data and classification is very important task which can help in finding the hidden information from the images and specialist to detect the affected area and disease early. In this paper image classification is our main objective and we try to understand that which type of data we need to classify and what are the classification techniques which we can apply to classify the medical images. Here we review on some classification techniques and we are mainly focus on the SVM (Support Vector Machine) technique. It is widely used today because it handles the multi dimensional data and gives the best and accurate result.

A In the medical field, the Image dispensation techniques are extensively employed for image amelioration in finding and treatment of lung disease in the big data environment, where the point in time feature is very paramount to determine the idiosyncrasy issues in intention images, particularly in lung disease such as cancer, pneumonia, COVID-19 etc, for early detection and treatment stages of lungs disease, Image processing technique are widely used for identification of genetic as well as environmental factors are very important in developing a novel method of lung disease prevention The core factors of this research are quality, time, and precision of the dataset The modification and evaluation of image quality depend on the segmentation techniques, an improved area of the object that is utilized as a rudimentary substructure of feature extraction is obtained and comparison is made on relying feature Medical images are analyzed by different segmentation techniques of image processing The segmentation techniques are used dataset to find patterns and retrieve information from the dataset for processing The goal of this study is discussed various image processing techniques and big data analytics tools for lung disease has been given in the tabular form and provides comparative study This study provides minutiae of big data analytics tools and image processing techniques, specifically discussed in the context of lung disease images © 2021 Institute of Physics Publishing All rights reserved

Image segmentation is the most crucial part in image processing techniques. Numerous segmentation techniques are used to segment digital images into smaller regions called segments, consisting of sets of pixels in order to analyze important information from the images. Segmentation simplifies the process of information retrieval from the region of interest (ROI).It helps in converting the digital image into more relevant information and easier to analyze. This paper presents a comparative analysis of existing segmentation techniques and its modification to form new segmentation techniques to overcome some of the drawbacks of the existing image segmentation approaches.

Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations; overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy.

All Start In this examination as learning, a dataset of COVID-19 lung contaminated X-beam pictures with common infection pneumonia, insisted Covid-19 sickness, and ordinary occurrences were utilized for the programmed acknowledgment of the Corona virus infirmity. The purpose of the investigation is to the comparable portrayal of Covid-19 utilizing Convolution Neural Network and assesses the precision proposed model convolutional neural framework over the progressing years for clinical picture characterization. Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. To survey the hypothesis accuracy of the models. The plan models end up being productive appeared differently in relation to made by (Arpan Mangal et al, 2020), (A. A. Saraiva et al.,2020), (Ioannis D. Apostolopoulos et al, 2020), (Ali Narin et al. 2020), which gained accuracy 90.5,95.30, 96.78, 98 independently and the current work had an ordinary precision of 99.54.

In this world of digitized era there is immense desideratum of image processing which manipulates the image information for analysis purport. Any image can be characterized by its feature sets, which demonstrate the detailed information of the images and enhance their visual interpretation. The features are extracted corresponding to the whole image comprised of both ROI (region of interest) and Non-ROI regions. Additionally, the region of Non-ROI shows promising results for obnubilated information corresponding to expected disease. So, for that purport we have proposed a method that extract the feature from the ROI and the Non-ROI region corresponding to the mark vertices in an image utilizing mouse click. The proposed work utilizes a model that extracted the categorical region by culling the concrete area and marks the vertices, double click at the last vertices utilizing mouse, the area automatically extracted corresponding to the masking that is discretely exhibited. The proposed work uses two neuroimaging modalities such as CT-Scan and MRI. As per the survey of radiologist, Medico and medicos, most of the time they concentrated only those components of the image i.e. ROI that can be facilely detectable and shows the cause of disease. They have to leave the rest of the component of the image i.e. Non-ROI that can be cause of the further detection of disease. In furthermore we extracted the obnubilated consequential information from the unattended portion of the neuroimaging modalities i.e. Non-ROI.

Big Data refers to the collection of various kinds of data in a huge amount. Big Data in medical field refers to some clinical datasets and medical images. This paper discusses about medical images and their enhancement. Analysis of medical images is very important in the area of health care. With the help of the systems used to analyze Big Data, we can analyze medical images. In this paper, we are going to perform image enhancement on medical images for better understanding of these images. There are various tools for this purpose. This paper presents Big Data analytical tool to perform image enhancement on these images in a parallel distributed environment.

Model order reduction has been done of linear time invariant (LTI) systems by utilization of Singular Perturbation Method (SPM). In this method Eigen values of full order original system are divided into "slow" and "fast" subsystems depending on the dominating and non-dominating Eigen values. First of all the system matrices A, B, C and D are transformed into Controllable Canonical Form (CCF) as the partition matrix A 22 must be non singular. The result obtained using proposed method is almost same as that of full order system and comparable with other popular methods of model order reduction. Singular perturbation method also retains the stability of original higher order system. The effectiveness of this technique is illustrated by taking one example.

The Lucas polynomials are famous for possessing wonderful and amazing properties and identities. In this paper, Diagonal function of k-Lucas Polynomials is introduced and defined by Gn+1(x)=kxGn(x)+Gn-2,(x), n≥1. with G0(x)=2. and G1(x)=1 Some Lucas Polynomials, rising & descending diagonal function and generating matrix established and derived by standard methods.

The Fibonacci sequence, Lucas numbers and their generalization have many interesting properties and applications to almost every field. Fibonacci sequence is defined by the recurrence formula Fn=Fn-1+Fn-2, , and F0=0, F1=1, where Fn is a nth number of sequence. Many authors have been defined Fibonacci pattern based sequences which are popularized and known as Fibonacci-Like sequences. In this paper, Generalized Fibonacci-Like sequence is introduced and defined by the recurrence relation Bn=Bn-1+Bn-2, with B0=2s, B1=s+1, where s being a fixed integers. Some identities of Generalized Fibonacci-Like sequence associated with Fibonacci and Lucas sequences are presented by Binet’s formula. Also some determinant identities are discussed.