Quantum computers consist of a quantum processor – sets of quantum bits or qubits operating at an extremely low temperature – and a classical electronic controller to read out and control the processor. The machines utilize the unusual properties of matter at extremely small scales – the fact that a qubit, can represent “1” and “0” at the same time, a phenomenon known as superposition. (In traditional digital computing, transistors in silicon chips can exist in one of two states represented in binary by a 1 or 0 not both).

Under the right conditions, computations carried out with qubits are equivalent to numerous classical computations performed in parallel, thus greatly enhancing computing power compared to today’s powerful supercomputers and the ability to solve complex problems without the sort of experiments necessary to generate quantum phenomena.

this technology is unstable and needs to be stored in a cool environment for faster and more secure operation.In this paper, we discuss the possibility of integrating quantum computers with electronics at deep cryogenic temperatures.

These days technology plays enormous roles in making day to day human activities easier. Previously established institutions need to reconstruct their data centers to run their business in an easy and comfortable manner. information and communication technologies (ICT) used for progressive improvement in the quality of education. in most developing country education disseminate rate is low with different factors poor of infrastructure, well engaged and capable teachers, geographical locations, different education tools, laboratories and also budgeting issues are the main challenges, as government higher education can invest more budget and they can deploy different clouds for a society at least one public university in one state have responded to deploy a cloud.  they can distribute centrally different educational information’s, books, and so on, again not expected that all schools are developing their own datacenter and full fill different computing infrastructure’s which cost more instead with this scenario all schools including primary, secondary and university’s mostly fourth-generation universities since they may take a lot of time to deploy their infrastructures until they can use another’s universities cloud minimizing costs and manpower.

Smart communities are the result of the evolution of communities under the development of new technologies. Many cities regard smart communities as one of the three regional demonstrations of smart city construction. Based on the experience of the construction of excellent intelligent communities at home and abroad, this paper analyzes the problems and difficulties in the current smart community construction, puts forward specific feasible countermeasures, and designs the smart community structure, which can promote the humanization of community governance, the integration of community services and the intelligentization of home life.

For a different item, you can purchase it. There are different costs to purchase that you can analysis the benefit and loses, even if on the software it has different approaches to analysis their wages, the developer company can get their analysis by the different methods.  There are a lot of software cost estimation methods are appearing in different years but still, those methods have their own drawback on making a correct effort and scheduling estimation, here in the paper Today there are different software cost estimation methods that the software company uses from the requirement to implementation phase. Corrected cost estimation supports us to complete the project on planned times and budgets. This paper is mainly presenting the current situations of cost estimation on software placed in Addis Abebas different software compony

This paper is aimed at development of a college database management system that can be used in any educational institution. Python flask framework is used to implement this application, that can be accessed throughout the institution or by any specific department of the institution. This system offers different features to the students and the members of the staff which includes attendance and marks  of the students that can be accessed by both student and staff but can be only updated by staff of particular department . Students as well as staffs have separate logins that can be accessed by them easily . The system  also includes a library management system which works in real time,a micro transport management system and a training and placement management system.Library management system keeps track of the books issued and also calculates the fine generated for delay in book returning and transport system maintains the record of each student i.e bus number ,route of that bus etc . A separate system for analysing the placement record of the college is also present . This system is developed  to maintain and facilitate easy access to information to every member associated with this system. For this the users must be registered with the system after which they can access their accounts as well as modify data as per the permissions given to them.

Python flask framework is the technology that we have used to develop the college management system. Flask is a web framework written in python. It can be classified into microframework because it does not require any particular tolls or libraries. However flask supports extension that can add application features . For frontend we have used HTML,CSS and javascript whereas for backend we have used python flask and mysql.This system  helps college and universities to save time , money and resources. 

One of major component of operating system is task scheduling for the optimum utilization of the resources. Round Robin had been an effective task scheduling method so far, but it has certain limitations. It uses static time quantum which sometimes leads to starvation.  The proposed Optimised Round Robin is a modified version of the existing Round Robin scheduling which results in better average time and average turnaround time and overall increase in the performance. The comparative analysis is being done that indicates ORR gives improvement in the system performance.

At the point while setting up the submerged acoustic sensor system (UASN), hub deployment will be the above all else errand, whereupon numerous basic system administrations, for example, system topology control, directing, & limit recognition, will be manufactured. Where hub deployment at 2-D physical remote sensor systems had been broadly concentrated, little consideration must be gotten through the 3-D partners.
This paper goes for examining the effects for hub deployment procedures at localization exhibitions at the 3-D environment. All the more particularly, these re-enactments directed at this project uncover which of general tetrahedron deployment plan outflanks these irregular deployment plan & this 3D shape deployment plan as far as decreasing localization blunder & expanding localization proportion by keeping up these normal number for neighbouring anchor hubs & system availability. Given this way which arbitrary deployment will be essential decision for greater part of viable applications for date, the outcomes will be relied upon for reveal some insight onto the outline UASNs sooner rather than later.

Now a day the usage of credit cards and net banking for online payments has dramatically increased. The most popular mode of online as well as regular purchase payments is through credit card and security of such transactions is also a major issue as frauds are increasing rapidly. In the existing scenario, fraud is detected after the transaction is done and it makes more difficult to find out fraudulent loses barred by issuing authority. In this paper, we observe the behaviour of credit card transactions using a Hidden Markov Model (HMM) and show how it detects frauds. An HMM is initially trained with the normal behaviour of transaction. If the present credit card transaction is not accepted by the trained HMM with enough high probability, then it declares as a fraudulent transaction. At the same time, we try to ensure that no genuine transactions are rejected.

K-Nearest Neighbor (KNN) classification is one of the most fundamental and simple classification methods. It is among the most frequently used classification algorithm in the case when there is little or no prior knowledge about the distribution of the data. In this paper a modification is taken to improve the performance of KNN. The main idea of KNN is to use a set of robust neighbors in the training data. This modified KNN proposed in this paper is better from traditional KNN in both terms: robustness and performance. Inspired from the traditional KNN algorithm, the main idea is to classify an input query according to the most frequent tag in set of neighbor tags with the say of the tag closest to the new tuple being the highest. Proposed Modified KNN can be considered a kind of weighted KNN so that the query label is approximated by weighting the neighbors of the query. The procedure computes the frequencies of the same labeled neighbors to the total number of neighbors with value associated with each label multiplied by a factor which is inversely proportional to  the distance between new tuple and neighbours. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the significant improvement in the performance of KNN method.

Image enhancement techniques are prominently used to analyze the image by enhancing key factors like contrast, resolution, and quality of the image. With the proper analysis of images, it is desirable to pre-process the image for resolution and contrast enhancement. We present here a new approach based on discrete wavelet transform (DWT), singular value decomposition (SVD) for image contrast and resolution enhancement, The contrast of the image is enhanced by maximum value fusion technique applied to the images created by using modified cuckoo search algorithm (CSA) and singular value decomposition separately. The masking approach is employed, for obtaining residual pixel value between original and scaled images independently. The resolution of the image is enhanced by combining interpolated high-frequency sub-band and maximum value fusion images. The proposed algorithm helps to minimize the noise artifacts and over enhancement problems. Experimental results are tested in terms of peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE). The proposed method shows better performance compared to other contrast and resolution enhancement techniques.

In the present scenario the health is the prime concern for every of us. Due to the increase in pollution and the other contaminants in environmental atmosphere the chances of getting a disease is became a common factor. No, doubt we are very cautious to prevent ourselves but still due to because of busy schedule we can’t able to take a proper precaution. Hence the medical science in association with modern technology and with the contribution of AI, has invented various gadgets using which we can take a precaution at earlier stages of disease. These devices are using the secure mode of data transmission for transferring the data in private mode. Here in this paper we are going to discuss the various aspect of medically analysis and detection of disease in Health care.

Nowadays, more and more organizations are realizing of importance of their data, because it can be considered as an important asset in present nearly all business organizational processes. Information Technology Division (ITD) is a department in the International Islamic University Malaysia (IIUM) that consolidates efforts in providing IT services to the university.

The university data management started with decentralized units, where each center or division has its own hardware and database system. Later it improved to become became centralized, and ITD is now trying to one policy across the whole university. This will optimize the high performance of data management in the university. A visit has been done to ITD building and a presentation has been conducted discussing many issues concerning data management quality maturity in IT division at IIUM. We got some notices like server’s room location, power supply and backup and existence of data redundant. These issues are discussed in details in the next sections of this paper and some recommendations are suggested to improve data quality in the university.

The quality of the data is very important in decision making, especially for a university that is trying to improve its strategy towards a research university and rise its rank among the World University Ranking.

In the world with increasing globalization , where money places a crucial role in determining the expansion and earnings of a company trading places a very crucial role. Multiple companies invest millions and billions of dollars in other countries with an expectation to make profits. In such a risky business Predicting the movement of the market can help companies or individual in making good decisions and can prevent severe loses. In this research paper we will discuss how we can use the computational power of the computer on cloud along with the machine learning algorithms to predict the closing values of the stocks which is a big challenge otherwise. For this purpose we will use Python as our programming language which supports a lot of ML based Libraries. The models we will be using are SVM(Support Vector Machine) , Linear Regression , Random Forest, XGBoost ,LSTM for deep learning

Selection and placement of appropriate personnel for the right job leads to great success in any organization. However, this is one of the most important activities carried out by Human Resource (HR). Minimizing imprecision and subjective value judgment in personnel selection processes were taken into consideration in this research by developing personnel selection and placement framework using the Mamdani- fuzzy model. This research work is aimed at developing Mamdani- fuzzy framework for academic staff selection and placement. A model with three levels has been developed to manage the database, and the necessary conditions required from applicants for selection and placement, and the consideration of individual temperament was paramount. Tools: Java script and HTML (for the front end) PHP and MySQL (for the database storage as back end).  Experimental results using fuzzy classification membership function defined by the truth value of a fuzzy propositional function would also be used as part of the analysis and design. When the need arise, MathLab would be employed for some analysis and simulations. A graphical user interface (GUI) would be developed for all the relevant forms in order to effectively interact with the users of the system

Lifetime enhancement has always been a crucial issue as most of the cognitive radio network (CRNs) operates in unattended environment where human access and monitoring are practically infeasible. Clustering is one of the most powerful techniques that can arrange the system operation in associated manner to attend the network scalability, minimize energy consumption and achieve prolonged network lifetime. To conquer this issue, current researchers have triggered the proposition of many numerous clustering algorithms. However, most of the proposed algorithms overburden the cluster head (CH) during cluster formation. To overcome this problem, many researchers have come up with the idea of fuzzy logic (FL), which is applied in CRN for decision making. These algorithms focus on the efficiency of CH, which could be adoptive, flexible, and intelligent enough to distribute the load among the sensor nodes that can enhance the network lifetime. But unfortunately, most of the algorithms use type-1 FL (T1FL) model. In this work, we propose a clustering algorithm on the basis of interval type-2 FL model, expecting to handle uncertain level decision better than T1FL model.

Heart disease is considered as one of the major causes of death throughout the world. Classification of heart disease can be valuable for medical practitioners to predict disease at early stage. To get accurate result of prediction of disease medical diagnosis system     very useful. There are different data mining techniques available to classify medical data related to heart disease. In this paper, survey is carried out to find which are different data mining techniques can be used for heart disease prediction system.

Online Analytical Processing (OLAP) is playing a vital role in today’s business work as well as in other domain. It gives the users an analytical view of data resulting in effective decision making. Many domains are applying Online Analytical Processing (OLAP) for different kinds of analysis due to its excellent performance and wide adaptability, such as analysis of network traffic and remote sensing images. The objective of this work therefore is to develop a web result portal that will enable lecturers post and compare students’ results for quick assessment. I was motivated in order to reduce stress, workload and discomfort always encountered by the lecturers and school examination body during computation, compilation and reconciliation of students’ results. Object-Oriented Analysis (OOADM) will be adopted as the methodology while Java will be used as the programming language. The expected result is web-based software for students result processing that will eliminate the problems of manual database system used in tertiary institutions.

This seminar paper deals with biometric authentication, using human facial geometry as the login verification parameter. Biometric is the measurement and statistical analysis of human's unique physical and behavioral characteristics by mapping face geometry, fingerprints, iris, and voice. The aim of this seminar paper is to develop a functional Biometric Authentication System Using Face Geometry as the authentication method. Facial Geometry Authentication is a category of biometric technology that maps an individual's facial features mathematically and stores the data as face-prints in a database. Meanwhile the objective of this paper is to improve data access security, enhance identification accuracy, and contribute to the improvement of the existing facial recognition systems, focusing mainly on increasing its accuracy performance. On the other hand, hacking into users’ privacy, loss of confidential information, and high running costs, among others geared the motivation to develop a biometric authentication system using face geometry. Furthermore, the software engineering methodology adopted in this seminar paper is the Structural System Analysis and Design Methodology (SSADM). The software is developed in Visual C-sharp, and the database in SQL Server 2012; using Microsoft Visual Studio 2012 as the integrated development environment. Besides, OpenCV 2.4.8 library is used for image processing, image mapping, and computer vision.  The expected result of this seminar paper include: improved user data security, and increased efficiency in facial detection/recognition.

Image processing plays a one of the important vital role in developing real world application is an  Image Segmentation, which is used widely in  Computer vision for the purpose of object tracking and to identify image boundaries. It aims at extracting meaningful objects lying in the image. Generally there is no unique method or approach for image segmentation The different algorithms used in Image segmentation are Clustering-based, Region-based and Edge based. Image segmentation is the division or separation of an image into multiple segments i.e. set of pixels, pixels in a region are similar according to some criterion such as color, intensity or texture. This paper gives the view about the methods in image segmentation such as thresholding, k-means clustering, grab-cut method and graph -cut method. Every method is discussed along with its advantage and disadvantages which helps us in deciding which the best and efficient method of image segmentation is. The main aim of the paper is to come out with the more efficient method in image segmentation which can be used for real world application development.

Digital watermarking has become a promising research area to face the challenges created by the rapid growth in distribution of digital content over the internet. To prevent misuse of this data, digital watermarking techniques are very useful. Here, a new algorithm that can be used for copyright protection is introduced for the embedding and detection of a watermark in the domain of discrete wavelet transform (DWT). The elements of the logo watermark are embedded directly to the one-level DWT decomposed sub-bands. The experimental tests and results of this scheme have shown that the scheme has the desired properties such as invisibility and robustness