Images Etc Research Articles

Conventional standard-sized bra with prosthesis or patient’s bra with customized hand-knitted external prosthesis after mastectomy: Mixed-methods evaluation of patients’ preferences

INTRODUCTION: In breast cancer patients undergoing mastectomy without reconstruction, an external breast prosthesis could aid patients’ recovery, improve body image and confidence by helping to regain a symmetrical chest appearance when dressed. However, external breast prosthesis preferences among Asian breast cancer patients were not widely studied. We aimed to compare patients’ experience with the conventional commercially manufactured standard-sized (small, medium, large, extra-large) bra and prosthesis versus customized hand-knitted external breast prosthesis with patient’s bra after unilateral mastectomy at a tertiary hospital. This is the first such study in Asian women, to our knowledge. METHODS: In this prospective study, participants used the conventional bra-prosthesis followed by the customized one consecutively, each for at least 3 months before they were administered an identical questionnaire at 3 and 6 months respectively. The questionnaire assessed the patients’ experience with the prosthesis on the aspects of comfort, body image and satisfaction etc. Patients were also invited for in-depth interviews. RESULTS: Of 155 eligible patients, 148 patients participated with a response rate of 95.5%. 99 (67%) participants preferred the customized prosthesis, while 38 (25.7%) did not. 11 (7.4%) participants were undecided. Seventeen participants underwent in-depth interviews until data saturation on major qualitative themes was achieved. More patients experienced excessive sweating (p < 0.0001), greater discomfort (p = 0.0195) and higher rates of prosthesis dislodgement (p = 0.0269) with the conventional bra-prosthesis. CONCLUSION: Customized external breast prostheses could be an alternative to the conventional ones for breast cancer patients with mastectomy, with additional benefits of less sweating, more comfort and less dislodgement perceived.

A new approach of contrast enhancement for Medical Images based on entropy curve

X-ray, MRI, and CT scan images etc, are widely used for the detection of various prevalent diseases. The efficient identification of the disease depends very much on the quality of the image. Sometimes due to environmental conditions such as insufficient and nonuniform illumination, shacking and movement of objects or the imaging device, and some other fault in the device, the resultant image may have low contrast and the presence of noise or artifacts. In such situation, a contrast enhancement algorithm that can efficiently improve the contrast without increasing noise and artifacts can play an important role. In this article, we propose a simple yet efficient method for these types of images by using the image’s entropy curve and homomorphic filtering. It enhances the contrast of medical images without increasing the content of noise and artifacts. Also, the performance of the proposed method is evaluated by a complete qualitative and quantitative analysis.

Image representation and reconstruction by compositing Gaussian ellipses

AbstractIn this paper, a method of stroke‐based rendering is proposed for image representation and reconstruction. The proposed method involves compositing a set of ellipses that greatly vary in location, size, rotation angle, color, and opacity. This study proves that alpha compositing is differentiable if a two‐dimensional Gaussian function is used to draw a solid ellipse. The gradient method can then be employed for automatically identifying the parameters of each ellipse such that the difference between the input image and the composited image is minimized. Experimental results indicate that the proposed method can represent various types of images including portraits, landscapes, buildings, street scenes, artificial objects, medical images etc. The proposed method can particularly render the most details and significant features of an input image with fewer strokes compared to other stroke‐based rendering algorithms. This study also demonstrates that the proposed method can be applied in painting style simulation and sparse‐view computed tomography imaging.

Irreducible quasi-D-overlap functions: Matrix representation and diagonal generation

As an emerging mathematical model of information aggregation successfully applied in image processing, pattern recognition, decision-making and etc, theoretical research of overlap functions has also continued to develop, especially the research on their related derivative concepts that have emerged in recent years. This work considers so-called irreducible quasi-D-overlap functions defined on grid of unit square, which are one class of discrete aggregation operators originated from overlap functions. The core contents are: (i) it presents the concept of irreducible quasi-D-overlap functions and shows basic properties of them; (ii) it provides equivalent characterization of irreducible quasi-D-overlap functions by means of strong permutation matrixes; (iii) it gives a construction manner for irreducible quasi-D-overlap functions by means of so-called diagonal operators and the greatest and smallest irreducible quasi-D-overlap functions with given diagonal operator are obtained.

Image Retrieval Through Free-Form Query using Intelligent Text Processing

Image Retrieval is the process of retrieving images from the image/multimedia databases. Retrieval of images are carried out with various types of queries, free-form query is a text-query that consists of single or multiple keywords and/or concepts or descriptions of images with or without the inclusion of wild-card characters and/or punctuations. This work aims to handle image retrieval based on free-form text queries. Simple & complex queries of conceptual descriptions of images are explored and an intelligent processing system with free-form queries based on the Bag-of-Words model is modified and built for natural scene images and on Diverse Social Images using the Damerau-Levenshtein edit distance measure. The efficacy of the proposed system is evaluated by testing 1500 free-form text queries and has resulted in a recall accuracy of 91.3% on natural scene images (of Wang/Corel database) and 100% on Diverse Social Images (of DIV400 dataset). These results show that the system proposed has produced satisfactory performance compared to published results such as the harmonic mean of precision and recall (i.e. F1-Score) of 76.70% & 63.32% at retrieval of 20 images etc in reported works.

Deep Image Registration With Depth-Aware Homography Estimation

Image registration is a basic task in computer vision, for its wide potential applications in image stitching, stereo vision, motion estimation, and etc. Most current methods achieve image registration by estimating a global homography matrix between candidate images with point-feature-based matching or direct prediction. However, as real-world 3D scenes have point-variant photograph distances (depth), a unified homography matrix is not sufficient to depict the specific pixel-wise relations between two images. Some researchers try to alleviate this problem by predicting multiple homography matrixes for different patches or segmentation areas in images; in this letter, we tackle this problem with further refinement, i.e. matching images with pixel-wise, depth-aware homography estimation. Firstly, we construct an efficient convolutional network, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPH-Net</i> , to predict the essential parameters causing image deviation, the rotation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> ) and translation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> ) of cameras. Then, we feed-in an image depth map for the calculation of initial pixel-wise homography matrixes, which are refined with an online optimization scheme. Finally, with the estimated pixel-specific homography parameters, pixel correspondences between candidate images can be easily computed for registration. Compared with state-of-the-art image registration algorithms, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPH-Net</i> has the highest performance of 0.912 EPE and 0.977 SSIM, demonstrating the effectiveness of adding depth information and estimating pixel-wise homography into the image registration process.

МАРКЕРИ ЕТНОКУЛЬТУРНОЇ СВІДОМОСТІ УКРАЇНЦІВ (НА ПРИКЛАДІ ІСТОРИЧНОЇ ЕПОПЕЇ ІВАНА ОГІЄНКА «НАШ БІЙ ЗА ДЕРЖАВНІСТЬ»)

The article outlines the main lexemes with ethnocultural marking, used in the historical epic Ivan Ohiienko «Our fi ght for statehood». The idioms, presented in the analyzed text fragments, which contain an ethnonational component in their structure, directly refl ect the worldview and thinking of Ukrainians. Ivan Ohiienko, researching the history of the formation of the Ukrainian state, very aptly emphasized the features of Ukrainians as bearers of national culture, which contained connections with vari-ous spheres – thinking, worldview, worldview, world understanding, speech, cultural interpretation etc. These deep structures of human intellectual activity in their interre-lationships created a unique code of national culture of Ukrainians who defended and continue to defend Ukrainian independence all their lives. In the historical epic of the epic, Ivan Ohiienko not by chance uses lexemes to denote objects of spiritual national culture, covering the national, religious, partly mythological and territorial-geographic sphere. One of the central markers in the work is the lexeme Ukraine, which, accord-ing to the author’s beliefs, is a personifi ed component of the ethno-cultural space of Ukrainians. The evaluative meanings used to characterize the indicated lexeme are mostly negative (burns, cries, dies, burns, trembles, freezes). The description of the basic ethnocultural phytonyms (willow, viburnum, poplar, oak) used in the work tes-tify to the mythopoetic worldview of Ukrainians. A number of used toponyms (for example, Kyiv, Lubenshchyna, Volyn, Novgorod, Berestechko, Lviv, Rohatyn etc.) are representatives of the geographical location in the work. The inseparable connection with the Motherland is presented through the description of symbols – objects of a certain territory – hydronyms (Dnipro, Dniester, Black Sea, etc.). Considerable atten-tion in the work is devoted to the religious and spiritual sphere, which is represented through the use of lexemes church, God, metropolitan priest, Cross, image etc. In gen-eral, the meaning fi eld of the epic is ethno-cultural dominants broad and multifaceted, since such lexemes are in the verbal plane implement various types of interaction of Ukrainians in society at the level of comprehension and assimilation of cultural herit-age of previous generations

Design of digital image encryption based on elliptic curve cryptography (ECC) algorithm and Radix-64 conversion

The billions of bits of information are transferred each second through the internet every day. The information may be text, image, audio or video etc, accordingly, we need some protection mechanism while sharing confidential data. Generally, RSA algorithm is used for encrypting the Secret images. However, the security provided by Elliptic Curve Cryptography (ECC) is higher with lower sized key than the RSA algorithm. So, this article proposes an extended Elliptic Curve encryption approach for encrypting the secret images. In this system, the secret image is partitioned into three color image planes such as Red, Green and Blue. By applying Radix-64 encoding and Mapping table, these planes are converted into elliptic curve points and then these points are encrypted using ECC algorithm. Again, these points are applied to the Radix-64 decoding and the mapping table to get ciphered-image. At last, the key parameters such as a, b, p and Generator point (G) are embedded in the last four pixel positions of the ciphered-image. In order to get the original secret image, the recipient must extract these key parameters from the encrypted image and then apply the remaining processes to the encrypted image in the opposite order. Experimental results tested using MATLAB R2021b and it shows that the NPCR and UACI values are 99.54% and 28.73 % and better quality feature is attained since the entropy value is almost closer to eight. So, the proposed image encryption has robust capacity to fight against the differential attack.

DograNet – a comprehensive offline dogra handwriting character dataset

Abstract Handwritten Text Recognition is an important area of research because of growing demand to process and convert a huge data and information available in handwritten form to Digital form. The digital data instead of handwritten form can prove to be highly useful in different fields. Handwritten text recognition plays an important role in applications involved in, postal services, banks for cheque processing, searching of information and organization dealing with such applications. In text recognition application dataset of the specified script is required for training purpose. Datasets of the different languages could be found online but dataset of dogra script characters is still not available. This paper presents a Dogra handwriting character dataset which contains around 38690 character images etc grouped in 73 character classes extracted from 530 one-page handwritings of 265 individuals of having variable age, sex, qualification, location. The dogra character dataset would be freely accessible by scholars and researchers which could also be used for further recognition improvement and updating with more characters and word, Identification of writer, dogra word segmentation. Dogra dataset could also be used for extracting variation of handwriting according to age and gender.

A STUDY OF TECHNOLOGICAL EDUCATION SYSTEM IN INDIAN MUSIC AND ROLE OF TEACHER

Present conceptual research paper based on technological education system in our Indian Music. Today, everyone enjoys the convenience of technological devices such as cell phones, computers, i-pads, i-pods, notebooks, and faxes etc. which allow us to communicate globally within seconds. However, internet is the most popular form of communication at present in society due to its ability to interact globally from any location. The internet has changed broadcasts media in a most intriguing way. It has enabled everyone to create produce and share ones opinions, music, videos, thoughts etc. Technology should, however, not become the master; it should rather serve as a servant. Formerly known as IIMP, Music Database covers a broad cross-section of music periodical sources, from the most scholarly studies to the latest trends, including classical music, pop and popular music, rock, rap and hip-hop, blues, jazz, traditional and folk music, world music, music equipment and technology, recording techniques and technology, and the music, radio, and music video industries. On-line Conferencing Systems, sometimes referred to as Electronic Meeting Systems (EMS), are Internet-based services offering a virtual environment for real-time remote meetings between geographically dispersed participants. EMS are part of the broader field of Collaborative Internet Systems that encompasses the use of computers and Web technologies to support coordination and cooperation of two or more people attempting to perform a task or solve a problem together. The musical communication is the process of imparting/interchanging of thoughts and opinions through various Medias e.g. textual, audio, video, images etc. Communication is a constant process thought our lives both professionally and personally. The most important part of the internet is that it has made all these mediums readily available twenty four hours as long as one has internet connection. It seems to be taking over broadcast media in term of popularity and effective communication. The present paper focused on the study of Role of Information and Communication Technology (ICT) in Indian Music and Role of Teacher with prime objectives are (i) To understand the concept of ICT and Music in Education. (ii) To discuss the Role of Teacher in ICT Music in Education. (iii) To discuss the Self-Learning Resources (SLR) related to Musical Education. The methodology of the research is a different type involving an interpretative, conversation, observation and study secondary sources, like books, articles, journals, thesis, university news, expert opinion, and websites, etc.

Reviving Opportunities through Socio-Spatial Parameters for Community Interaction:

Within urbanized areas, the importance of neighborhood ponds should be assessed and investigated with its socio-spatial parameters (access and linkage, sociability, uses and activities, comfort and image etc) since such water bodies act as retention basin during floods, reservoir for lean period and responsive socializing agent to measure community interaction which are generally been neglected by city government, threatened by developers but taken care by neighborhood people in Old Dhaka. Most of the inner city small neighborhoods ponds have been encroached and filled up upon demand on land as the pond were not integrated in the city planning. The main objective of this research is to assess the significance of such water bodies and revive the spatial relationship of neighborhood ponds with its nearby residents. Therefore a socio-environmental survey (quantitative and qualitative) is held on neglected and threatened small neighborhood ponds of Old Dhaka with direct observation, in-depth interviews of the residents to compare the environmental, ecological and social contexts and explore the opportunities. An integrated assessment for enhancing sustainability and creating opportunities through community participation has been suggested to rejuvenate the ponds with attention for wetland preservation and to integrate into community development programs or planning.

A New super-resolution restoration method with Generated Adversarial Network for underground video images in coal mines

The computer can be used in Super-resolution reconstruction (SR) to process low-resolution images to obtain high-resolution images. Aiming at solving problems of complex underground video image acquisition environment, uneven brightness, blurred images etc, this paper adopts the idea of deep learning to perform super-resolution restoration of underground video images in coal mines, and proposes a generational confrontation network to super-resolution underground video images in coal mines. The experiment proves that Generated Adversarial Network (GAN), while being compare with Super-resolution Deep Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network (ESPCN), Deeply Recursive Convolutional Network (DRCN) the effect of GAN method is better, because it can better realize the super-resolution restoration of underground video images in coal mines and provide preliminary support for the subsequent and further application research of underground images in coal mines.

The Effect of Advertising and Consumer Satisfaction on Consumer Loyalty to Pepsodent Products in Banda Aceh City

This study aims to determine the effect of advertising and consumer satisfaction on consumer loyalty to Pepsodent products in Banda Aceh City. The population in this study were Pepsodent consumers in Banda Aceh City, with a total sample of 100 respondents using convenience sampling technique. The model used in this study is multiple linear regression analysis. The results showed that advertising and consumer satisfaction had a significant effect on consumer loyalty to Pepsodent products in Banda Aceh City by 80.2% and the remaining 19.8% was explained by other variables outside of this study such as product quality, brand image and price and etc. The value of the correlation coefficient (R) indicates that the relationship (correlation) between the independent variable and the dependent variable is 89.6%, meaning that consumer loyalty to Pepsodent products in Banda Aceh City has a strong and positive relationship with correlation advertisement(X1) and consumer satisfaction (X2). The results of the f test statistic test show that advertising and consumer satisfaction simultaneously have a significant effect on consumer loyalty to Pepsodent products in Banda Aceh City, with value Fcount&gt;Ftable (197,035&gt;3,090).While partially advertising and customer satisfaction have a significant effect on consumer loyalty to Pepsodent products in Banda Aceh City, with a value of tcount&gt; ttable (9.705 and 3.339&gt;1.984). Based on the results of multiple linear regression analysis, it can be seen that from the two variables studied, it turns out that the variable advertisement(X1) has the most dominant influence on consumer loyalty to Pepsodent products in Banda Aceh City, with a coefficient value of 0.689, and then followed by the variable consumer satisfaction(X2) with a coefficient value of 0.264.

Heart disease detection using deep learning methods from imbalanced ECG samples

Heart disease (HD) is a fatal disease which takes the lives of maximum people compared to other diseases across the world. Early and accurate detection of the disease will help to save many valuable lives. The HD can be detected from medical tests, Electrocardiogram (ECG) signal, heart sounds, Computed Tomography (CT) Images etc. Out of all types of detection of HD from ECG signals plays a vital role. In this paper, the ECG samples of the subjects have been considered as the required inputs to the HD detection model. In recent past, many useful articles have been reported for classification of HD using different machine learning (ML) and deep learning (DL) models. It is observed that with imbalanced HD data the detection accuracy is lower. With an objective to achieve better detection of HD, suitable DL and ML models have been identified in this paper and the required classification models have been developed and tested. The Generative Adversarial Network (GAN) model is chosen with an objective to deal with imbalanced data by generating and using additional fake data for detection purpose. Further, an ensemble model using long short-term memory (LSTM) and GAN is developed in this paper which demonstrates higher performance compared to individual DL model used in this paper. The simulation results using standard MIT-BIH show that the proposed GAN-LSTM model provides the highest accuracy, F1-score and area under curve (AUC) of 0.992, 0.987 and 0.984 respectively compared to other models. Similarly, for PTB-ECG dataset the GAN-LSTM model outperforms all other models with accuracy, F1-score and AUC of 0.994, 0.993 and 0.995 respectively. It is observed that out of the five models investigated, the GAN model performs the best whereas the detection potentiality of the NB model is the lowest. Further research work can be carried out by choosing all other different ensemble models and using other different datasets and the performance can be similarly obtained and compared. The proposed best detection methodology can also be applied to other diseases and healthcare problems.

Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors

Automated machine learning (AutoML) has achieved remarkable progress on various tasks, which is attributed to its minimal involvement of manual feature and model designs. However, most of existing AutoML pipelines only touch parts of the full machine learning pipeline, e.g., neural architecture search or optimizer selection. This leaves potentially important components such as data cleaning and model ensemble out of the optimization, and still results in considerable human involvement and suboptimal performance. The main challenges lie in the huge search space assembling all possibilities over all components, as well as the generalization ability over different tasks like image, text, and tabular etc. In this paper, we present a first-of-its-kind fully AutoML pipeline, to comprehensively automate data preprocessing, feature engineering, model generation/selection/training and ensemble for an arbitrary dataset and evaluation metric. Our innovation lies in the comprehensive scope of a learning pipeline, with a novel "life-long" knowledge anchor design to fundamentally accelerate the search over the full search space. Such knowledge anchors record detailed information of pipelines and integrates them with an evolutionary algorithm for joint optimization across components. Experiments demonstrate that the result pipeline achieves state-of-the-art performance on multiple datasets and modalities. Specifically, the proposed framework was extensively evaluated in the NeurIPS 2019 AutoDL challenge, and won the only champion with a significant gap against other approaches, on all the image, video, speech, text and tabular tracks.

Hybrid spatio-frequency domain global thresholding filter (HSFGTF) model for SAR image enhancement

Abstract Synthetic Aperture Radar (SAR) deploys single antenna for both transmitting and receiving radar signals since it comes under mono static category. Major research work based on airborne and orbital focused on moving vehicle capturing, base penetration, greenery or vegetation penetration, methodologies to improve the quality of the captured images etc. In general, synthetic aperture radar imaging provides high level resolution images of vast regions under coverage. The pixel intensity of images in synthetic aperture radar is measured based on roughness, dimensional orientation and dielectric constant of plane captured. Processing of SAR images is mapping of signal data received by the radar in to a spatial image. Synthetic Aperture Radar images are easily vulnerable to speckle noises. These kinds of noises disrupt the image elucidation. This research article proposed the hybrid spatio-frequency domain global thresholding filter based pre-processing for SAR image enhancement for removal of additive as well as speckle noise which is more characteristic of any SAR images. The proposed image enhancement algorithm has been experimented under different noise variances and the results obtained are compared with other traditional methodologies.

Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN

The face of a person plays a vital role in any communication or visual content. To enhance this visual content, popular and easy accessible editing tools are used. However, there malicious usage is spreading disharmony in the society, by tampering video evidences, defaming a person’s image etc. Therefore a robust detection method is required to authenticate the visual content. Thus, a novel method is proposed to detect facial forgeries. The proposed method extracts faces from a target video and convert them into frequency domain using two dimensional global discrete Cosine transform (2D- GDCT). Thereafter, a 3 layered frequency convolutional neural network (fCNN) is employed to detect forged facial image. The proposed method is trained and tested on FaceForensics++ dataset and Celeb-DF(v2) dataset. In addition, its robustness is evaluated on standardized benchmark dataset and compared with the state-of-the-art methods to prove its effectiveness.

Fabrication of eggshell derived hydroxyapatite polymer composite membrane for efficient removal of thorium ions from aqueous solutions

Abstract A novel eggshell-derived hydroxyapatite polymer composite membrane was fabricated to explore its efficiency in the elimination of Th(IV) metal ions from water bodies. Membrane fabrication was carried out by two stage process in which, at first hydroxyapatite (Hap) was derived from eggshell and the minerals thus obtained were used as filler in Poly vinyl alcohol (PVA) - Poly vinyl pyrrolidone (PVP) based polymer membrane. Structural and morphological studies of PVA-PVP-Hap membrane were carried out by analyzing Fourier transform IR spectra (FTIR), X–Ray diffraction spectra (XRD), Scanning electron microscope-energy dispersive X-ray images (SEM-EDX) etc. Water uptake capacity and ion exchange capacity were determined for membrane characterization. Optimization studies were performed for the elimination of Th(IV) metal from polluted water. Regeneration studies also proved that the membrane can be used upto 4 cycles of operation with less than 5% loss in performance using ethanol as eluent for the desorption of this pollutant from the membrane.

Fictionalising Trauma: Paratextual Analysis of Select Tamil Novels

Gerald Genette’s concept of paratext widens the horizons of literary canon in multidimensional approaches through textual materials that includes pictorial, title, author, font etc that acknowledges its key notions. In such context, innumerable modes of medium acts as a interlink to highlight the critical concept irrespective of prefixed protocols or notions embedded to the theory. On basis of such interpretation this paper attempts to relocate and redefine the projected idea through the layers of select Tamil novels and their Symbolic connectivity in conflicts of war, trauma, diasporic consciousness, photo realism, expressive images etc. Interpretation of parthenium plant with Srilankan war conflicts and its invisible political sketches through several imagery serves the primary essence of the paratextual sequence in Thamizhnadhi’s Parthenium. A thematic representation of carnatic music to project the cultural and geographical back ground of the protagonists embark on a journey to Paris and the quest for life well characterized in the novel Paris’ukku Po by Jeyakanthan. Vaikom Muhammad Basheer’s Tamil translation work of Balyakalasakhi gives the classic touch of artistic portrait through the brush strokes of certain unhappy occurrence in protagonist’s life. All the descriptions are analytically evolved and careful observations are systematically carried out in elitist view. Moreover the mere study of this paper well clarifies and serves the rich flavours of paratextual elements and traumatic factors that provides valuable and rich essence to the texts as well as clear understanding of the theory.

Issues and Challenges in Management related to Information Technology

There are many IT issues and challenges in management including Cloud Computing, Virtualization, New Technology, Big Data Analytics, BYOD (bring in their own devices) and BYOA (bring their own applications), Shadow IT, Boomers, Interoperability, User Systems, Energy Efficiency, Creating Value, Social Networks and so on. In this paper, we have highlighted three challenges: Cloud Computing, Virtualization and Social Networks. Over the last few years Cloud Computing, Virtualization and Social Networks has become modern trends in information technology space that increase the accessibility and flexibility for the users around the globe. Cloud Computing means providing computing services from the internet through internet-based tools and applications, without physically acquiring them. It offers faster innovation, flexible resources and economics of scale. Virtualization means better control on IT resources like applications, wide area networks, local area networks, servers, desktops, storage and appliances like firewalls and application delivery controllers. Social Networks means online service platform where user is able to build relationship with each other by message, posting information, images etc. It is the best way to connect people globally on one platform. Despite the success of cloud computing, virtualization and social networks face tremendous challenges in management such as security issues, customization, technology bottlenecks, lack of standardization, strategy issue, data lock-in, network traffic, software licensing, lack of profits, resistance etc. which are preventing management of organizations from accepting these techniques. This paper gives an overview of these technologies and identifies the different technical challenges in management and offer suitable direction to resolve them.