Computer Engineering

Existing and in recent times proposed clustering algorithms are studied and it is known that the k-means clustering method is mostly used for clustering of data due to its reduction of time complexity. But the foremost drawback of k-means algorithm is that it suffers from sensitivity of outliers which may deform the distribution of data owing to the significant values. The drawback of the k-means algorithm is resolved by k-medoids method where the novel approach uses user defined value for k. As a result, if the number of clusters is not chosen suitably, the accuracy will be minimized. Even, K-medoids algorithm does not scale well for huge data set. In order to overcome the above stated limitations, a new grid based clustering method is proposed, where time complexity of proposed algorithm is depending on the number of cells. Simulation results show that, the proposed approach has less time complexity and provides natural clustering method which scales well for large dataset.

Automatic annotation is actually a classification of medical images, using global and local features of images, IRMA standard code are extracted for them which consists of four data axes techniques of image providing (modality), direction, anatomy, and biological system. Recent researches show despite the fact that classification with high accuracy has been achieved good results but cannot always be optimal for all features of images. Therefore, in this paper, in order to improve the efficiency of automatic annotation system of medical images in terms of classification accuracy, the combined classifiers votes’ technique (Majority Voting) and Gabor filter feature in HSV color space for images has been used. The results are promising and improved performance show the improvement of the quality of the proposed system. In fact, in addition to high speed compared with previous methods, it has been achieved good accuracy as much as 75.1 percent

Automatic annotation is in fact the process of classifying medical using global and local features of standard image codes (IRMA) while being extracted. This includes four technical data axes of providing image (modality), direction, anatomy, and biological system. A number of recent researches have been conducted on the extraction of the local binary pattern texture features for automatic annotation, but until now no complete comparison has been conducted on the accuracy of the different classifications in resolution and annotation of the images based on the local binary pattern texture features. The results from the known and famous classifiers used on the four characteristics of anatomy, direction, biological system and modality are presented in this paper, which show that Sequential Minimal Optimization is the most efficient classification group as far as accuracy is concerned.

We present two high speed and low power full adder cells designed with modified pass transistor logic styles that lead to have a reduced power delay product (PDP) as compared to the previous logics DPL and SR-CPL logic. We carried out a comparison against other full adders reported as having a low PDP, in terms of speed and power consumption. All the full-adders were designed with a 0.12µm CMOS technology. Simulations of the circuit show that the proposed full-adders have reduced the power from 0.326mW to 0.242mW.

The Academic Management System is useful to both students and Faculties for Time table, Lesson Plan, Log Book upkeeping , viewing circulars sent by authorities , on-line assignment submission capability, Internal Marks entry, attendance and Mentoring and prepare the final report to be sent to parents. This system is developed as Web Application in ASP/ASP.Net for front end and MS SQL Server as the backend. Security is achieved by using SQL injections prevention codings and encrypted cookies and sessions. Username and password also given only in encrypted form. This automated implementation in our University environment has eased the work-load and enhanced the work efficiency of faculty members and students also feel happy to receive instantly all their Academic Information.

Given a collection of cities and the cost of travel between each pair of them, the traveling salesman problem, or TSP for short, is to find the cheapest way of visiting all of the cities and returning to your starting point. In the standard version we study, the travel costs are symmetric in the sense that traveling from city X to city Y costs just as much as traveling from Y to X. The Traveling Salesman Problem is typical of a large class of "hard" optimization problems that have intrigued mathematicians and computer scientists for years. Most important, it has applications in science and engineering I attempt to apply simulated annealing to find (Sub-Optimal) solutions to TSP with 100-200 cities randomly. The performance by changing the parameter values and try to understand how fast and effective simulated annealing algorithm can generate a solution to TSP problem.

Video on demand (VOD) service is widely used nowadays to watch online videos. Video on demand has evolved as a major Problem implementation for network. This research aims to design a suitable grid computing system to enhance performance of video on demand. The goal of using Grid computing system is to create the simple but large and powerful virtual computer with large collection of heterogeneous systems sharing resources. User can submit request for video to grid server without necessary knowing where it will be execute. It is the responsibility of the grid server to distribute request among several servers (clusters). The results we get optimize resource usage, load balance, and time delay for delivering video file. The MATLAB simulation program is designed to compare old server performance with Grid server performance focusing on time delay and system throughput, which decreases Video delay 60 % - 70%.

In this paper we presents a method for handling multiple metrics and different network parameters simultaneously to analyze the loss factor of routing protocols in mobile ad hoc network(MANET) environments. We have used Taguchi’ loss function to determine the best parameters giving maximum throughput, packet delivery ratio (PDR), average delay, DROP and routing over head simultaneously for AODV protocol. In this paper we have consider various different mobile ad hoc network parameters such as Terrain size, No of Nodes, No of source nodes, Packet transmission rate, Node speed, Pause time, Transmission range, Queue size, Antenna height and receiving power on a multiple signal to noise ratio (MNSR), performance and contribution level of parameters have been analyzed by analysis of variance (ANOVA). The analysis of results shows that the parameters which more affecting the AODV performance in mobile ad hoc networks are Queue size, Receiving power, Source node, Packet transmission rate, Antenna height and transmission range.

Visual Data mining (VDM) is extensively used and being studied field of research area now a days. We surveyed in this paper the basic idea behind the visual data mining. How and why we have to classify the VDM techniques and corresponding classification criteria’s. We also presented some of its advantages.

The massive amounts of data collected today by software in fields varying from academia to business and many other fields, is increasingly becoming a huge problem due to storage technologies not advancing fast enough to provide the performance scalability needed. This is even truer for data which are highly organized and require analysis while being stored in databases and being accessed by various applications simultaneously. As database vendors struggle to gain more market share new technologies emerge attempting to overcome the disadvantages of previous designs while providing more features. Two popular database types, the Relational Database Management Systems and NoSQL databases are examined. The aim of this paper was to examine and compare two databases from these two database models and answer the question of whether one performs and scales better than the other.

The volume calculation of MR Image segmented and estimate the affected intensity of Alzheimer’s disease is dealt with this paper. It is concerned with Voxel Based Morphometry to render the first part segmentation. The result gives an active region which further needs an estimation to justify the diagnosis. As in this case the image is in form of voxels. When properly processed, classified images can represent foundations for diagnostic purposes. A VBM - fuzzy approach was used to take advantage of Voxel Based Morphometry’s ability to fine segmentation based on voxel comparisons of GM, WM & CSF and membership degrees andfunctions of fuzzy logic, respectively. The method of VBM is done with SPM. The method is based on the spatial properties of the MR Brain image features and makes use of SPM multi-scaled representations of the image. A fuzzy classifier is created on basis of the previous segmented data. The method showed high quality classification for images of complex components in determining the intensity of Alzheimer’s and GUI is used as front end for the user comfort.

In this paper, we propose a novel joint data-hiding and compression scheme for digital images using side match vector quantization (SMVQ) and image inpainting. The two functions of data hiding and image compression can be integrated into one single module seamlessly. On the sender side, except for the blocks in the leftmost and topmost of the image, each of the other residual blocks in raster-scanning order can be embedded with secret data and compressed simultaneously by SMVQ or image inpainting adaptively according to the current embedding bit. Vector quantization is also utilized for some complex blocks to control the visual distortion and error diffusion caused by the progressive compression. After segmenting the image compressed codes into a series of sections by the indicator bits, the receiver can achieve the extraction of secret bits and image decompression successfully according to the index values in the segmented sections. Experimental results demonstrate the effectiveness of the proposed scheme.

The progression and multiplications in Information and Communication Technology (ICTs) has prompted movement of learning past the customary classroom Face-to-Face (F2F) to various sorts of adapting, for example, Distance Flexible Learning (DFL), Electronic Learning (eLearning) and to all over and whenever known as Mobile Learning (mLearning). [1] The broad utilization of versatile innovations has brought about expanding enthusiasm for portable learning. [3] In this paper we investigate the difficulties the cell phones has on learning and further talks about the outline and execution of android based understudy learning apparatuses which is worked to upgrade learning . The proposed framework has numerous learning devices, for the most part the auspicious notice for various modules, for example, exercises due dates, exam alarm and discourse notice. Incorporated with MOODLE (course administration framework) to upgrade learning, it additionally has instructional exercise apparatus for understudies/guides to decrease paper work and superfluous workload on the mentors. Other learning instruments gave to students are exam timetable, college news, exchange gathering, college and general data benefit. This proposed framework's devices gives greater adaptability, portability and advantageous than eLearning mode which rises routine consideration regarding self-learning modules which may support student's abilities in learning.

A trial was conducted during 2005 06 & 2006-06 at All India Coordinated Research Project for Dryland Agriculture Phulbani, Orissa ,India., with an objective to obtain the water loss and economics of the lined ponds .There were three treatments T1-Lined pond with soil cement plaster (6:1) 8cm thickness ,T2-Unlined pond,T-3-No pond.10% of the cropped area was dug for construction of the pond in Lined and Unlined pond treatments. The size of the pond is 7m top widths, 1m-bottom width, 3m heights, and 1:1side slope. The water harvested in pond was reutilized for the pumpkin crop, which was sown only in Lined pond treatment, as there was no water available in unlined pond so the crop was not sown there. Lined pond with soil cement (6:1) plaster of 8cm thickness gave highest Tomato yield of 4.8 t/ha during kharif 2008-09 and radish root yield of 25.5 t/ha in rabi seasons of 2008-09.. The water loss was 326 lit/day in lined pond and 24,000 lit/day in unlined pond. The benefit: cost ratio in lined pond was 3.04 as compared to 1.64 in unlined pond during 2008-09.

Internet of things is one of the rapidly growing fields for delivering social and economic benefits for emerging and developing economy. The field of IOT is expanding its wings in all the domains like medical, industrial, transportation, education, mining etc. Now-a-days with the advancement in integrated on chip computers like Arduino, Raspberry pi the technology is reaching the ground level with its application in agriculture and aquaculture. Water quality is a critical factor while culturing aquatic organisms. It mainly depends on several parameters like dissolved oxygen, ammonia, pH, temperature, salt, nitrates, carbonates etc. The quality of water is monitored continuously with the help of sensors to ensure growth and survival of aquatic life. The sensed data is transferred to the aqua farmer mobile through cloud. As a result preventive measures can be taken in time to minimize the losses and increase the productivity.

Cloud computing promises to significantly change the way we use computers and access and store our personal and busi-ness information. With these new computing and communica-tions paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user’s real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.

Being future technocrats we tossed this idea with an aim to provide service to the society. Providing an overture for the visually impaired, to make them feel self-reliant and thus raise their status in the society is the ultimate aim of our project. The proposed concept consists of a smart stick that generates voice output using the voice arrays and pre programmed microcontrollers. This tool encompasses five constructive features. The pre-eminent feature is the Obstacle Tracer which uses ultrasonic sensors to detect the obstacles on the person’s way and redirects them appropriately using voice commands. Area Detector uses the GPS module to figure out the location and gives the output as voice stream. Destination alerter generates a voice alert to the person on reaching a landmark (such as bus stop, bank, post office etc….) In addition to these our contrivance also has the Onboard Alerter module that informs the person when a bus enters the boarding area. This uses the transmitters in the bus and receivers in the contrivance. The Off Board Alerter specifies the subsequent bus stops as soon as the person boards on the bus. Thus our appliance will help the visually impaired person to be independent in his own path.

Mobile Agent is an alternate strategy to compose a dignified distributed system. Mobile agent is a program that can transform one host to another host at time in places of their choices (Jump and Go). The comparison between the Mobile Agent platforms Grasshopper and JAMES Framework as a suitable architecture for distributed system. A comparison is made and a categorization is made based on the performance of the James and Grasshopper mobile agents, with an emphasis on the James and Grasshopper framework as a viable architecture for distributed systems for multi-agent organizations. The purpose of this research is to compare and contrast mobile agent platforms.

In the modern cryptography, the problem of the generation of large primes is considered as an important issue to implement public key crypto-scheme such as RSA and so on. Since a great amount of computational resources is generally required to generate a large prime number, it takes considerable amount of resource especially in embedded systems. In general, the prime number generation is started from the random number generation. If the generated random number is passed a specified probabilistic primality test, the random number is tentatively considered as a prime number and applied to a public key crypto-system. In many primality tests, a number of modular exponentiations are usually required. Hence these algorithms are considerably slow. Here we are introducing a new prime generation technique using GA which can be used to generate large primes in more efficient way.

Biometric technology has become a basis of an extensive array of highly secure identification and personal verification solutions in our world today. More importantly in the wake of heightened concern about security and rapid advancements in communication and mobility. Significant application areas of biometric systems include security monitoring, access control and authentication, border control and immigration, forensic investigation, telemedicine and so on. When a single trait is used in an application it is referred to as unimodal biometric, while combination of two or more sources or traits in an application is referred to as multiple biometrics. But biometric system that uses a single biometric trait for recognition has this propensity to contend with problems related to non-universality of the trait, spoof attacks, large intra-class variability, and noisy data. Besides, no single biometric trait can meet all the requirements of every possible application, hence the need for multiple biometric system to overcome the limitation of unimodal biometric. The new paradigm is robust against individual sensor or subsystem failures and spoof attack, as it is very difficult to spoof multiple traits simultaneously. In addition, the technological environment is very appropriate because of the widespread deployment of multimodal devices (PDAs, 3G mobile phones, Tablet PCs, laptops etc). The aim of this paper is to present an overview of multiple biometric systems, design approach and application scenario.

This paper provides the simulation of 3 state inverter with different materials and compare the Leakage Current. Material which has the less Leakage Current is considered as better material for the designing of inverter. In previous time the mosfet is design by the combination of silicon and silicon oxide now we are using gallium arsenide and silicon dioxide as one combination and gallium arsenide and silicon nitride as another combination.

One of the basic areas of the computer science is Data Structure. Sorting is an important issue in Data Structure which creates the sequence of the list of items. Although numbers of sorting algorithms are available, it is all the more necessary to select the best sorting algorithm. Therefore, sorting problem has attracted a great deal of research as sorting technique is very often used in a large variety of important applications so as to arrange the data in ascending or descending order. This paper presents a Both Ended Sorting Algorithm which is faster or better than the bubble sort& others algorithm. After having studied various sorting algorithms; I came to the conclusion that there is no such sorting algorithm which works on the basis of both end comparison right end as well as left end. The new algorithm so is then analysed, implemented & tested. The test results obtained are then presented and compared with the traditional Sorting Algorithm. Worst case complexity is also improved as a compare to bubble sort.

Overlay text brings important semantic clues in video content analysis such as video information retrieval and summarization, since the content of the scene or the editor's intention can be well represented by using inserted text. Most of the previous approaches to extracting overlay text from videos are based on low-level features, such as edge, color, and texture information. However, existing methods experience difficulties in handling texts with various contrasts or inserted in a complex background. In this paper, we propose a novel framework to detect and extract the overlay text from the video scene. Based on our observation that there exist transient colors between inserted text and its adjacent background, a transition map is first generated. Then candidate regions are extracted by a reshaping method and the overlay text regions are determined based on the occurrence of overlay text in each candidate. In this paper represents comparative approach between edge and change in intensity models that are computationally fast and invariant to basic transformations like horizontal, Vertical and scaling. We demonstrate that change in intensity context can be used to detect overlay text regions. Intensity context describes all boundary points of the shape with respect to any single boundary point.

Abstract: Data mining is emerging technique now a day’s .The main aim of data mining is to find meaningful information from pool of dataset. It is also called knowledge Discovery in Database (KDD). Data mining is technique that extracts the important information from the large amount data base. Clustering is significant key technique in data analysis and data mining application. It is used for combining observed object into groups or cluster such that each group or cluster is homogeneous or compact with respect to characteristics. The clustering is an example of unsupervised learning. A good clustering method will produce high superiority clusters with high intra-class similarity and low inter-class similarity. Clustering algorithms can be categorized into partition-based algorithms, hierarchical-based algorithms, density-based algorithms and grid-based algorithms. The aim of this survey is to provide an analysis of different clustering algorithms in data mining.

In the recent era, the extensive and rapid growth in internet technology is creating a important need to develop several newer techniques to protect authentication, ownership, copyright and content integrity of digital data. A proposed solution to this kind of problem is to digital watermarking. Digital watermarking has viable technique to the need of authentication, ownership, copyright and content integrity of digital data. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are two most popular tools used in watermarking algorithm to protect digital image. Aim of this paper is to provide robust technique based on combined DWT and DCT propose a new watermarking algorithm for digital image. Discrete wavelet transform is used to provide robustness and discrete cosine transform has shorter elapsed time, taking advantages of both this technique proposed a new digital image watermarking algorithm. Results show that this algorithm combines the advantages of these two transforms.

Plant diseases are widespread by a variety of pests, weeds and pathogens and can have devastating effects on agriculture if not treated in time. Farmers face a myriad of challenges due to adequate water supply, premature rain, storage facilities, and diseases of multiple crops. Plant diseases are a major threat to farmers with enormous production and economic loss. Identifying disease on a few hectares of farmland can be a daunting task, even in the presence of modern technology. Accurate and rapid disease prediction for early crop disease treatment has proven to be productive for healthy crops as well, minimizing personal financial loss. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. Crop diseases are a constant challenge for smallholders, threatening income and food security. The proliferation of smartphones and recent revolutions in computer vision models has created a method of image classification in agriculture. Convolutional neural networks (CNNs) are considered the cutting edge of image recognition and offer the possibility of making timely and clear diagnoses. This article examines the performance of a pre-trained ResNet 34 model in the detection of plant diseases. The developed model can be used as a web application to detect seven plant diseases from healthy leaf tissue. A dataset containing images of 8,685 leaves. Captured in a controlled environment and established for model training and validation. Verification results show that the proposed method can achieve an accuracy of 97.2% and an F1 score of over 96.5%. It demonstrates the technical feasibility of CNN in plant disease classification and points the way to AI solutions for smallholders.

In this paper we describe some recent developments that have taken place in still image compression. We addresses about different compression techniques and also give a performance comparison. Functionality and principles of compression algorithms are discussed in a unified manner. This survey reviews more recent articles on image compression and discuss their role in current research directions. There are several image compression algorithms some of them are lossy and some of them are lossless. Thus medical image, pre press industry, art work, remote sensing images for lossless image compression.

Cloud computing is the emerging technology in IT industry Cloud Computing helps the startup companies to implement their ideas and grab opportunities without worrying about the capital needed to start their Business plan. It is built on the concept of virtualization, utility computing, networking and Autonomic computing. The aim of this paper is to provide better understanding of the security challenges of cloud computing and analyze the research direction in this widely uses technology affecting people . In this paper, we present the prospect of cloud computing, its architecture and pros and cons of Cloud Computing .

Communities with different profiles shows the interests of community members. User and check-in venue details are used to cluster. Multimode multi-attribute edge-centric coclustering model is help to discover overlapping the communities. Overlapping communities is used to repair by replacing each edge with its vertices in edge clusters. Intermode and intra mode features are helped to us for making the process. Three intra mode features are used in the community detection process. M2 Clustering algorithm is used for community detection it is Edge clustering based on k-means and HM2 Clustering algorithm is used to detect overlapping communities of LBSNs and also called as Two-step hierarchical edge clustering. The overlapping community detection mechanism is enhanced with recommendation models. LBSNs, analyzing 1) the data source used, 2) the methodology community. 3) the objective of the community. The community are also classified with location. The system is enhanced with feature selection and feature fusion mechanism. This system also indicate the cluster of the two communities.

The purpose of this research is review of rules and application area of predictive data mining in medical sciences and presenting a frame work for creating, evaluation and exploitation of data mining models in this. In this paper a new method is presented for the diagnosis of diabetes. In this article we want to use the combination of artificial intelligent techniques such as genetic algorithm for attribute reduction from 8 to 4, the artificial neural decision tree to select the best possible conditions Neural Network, and education to estimate adaptability learn Machine, in order to identify the diagnosis of uses. The present study compared to older methods and meta-heuristic model involving a combination of genetic algorithms and neural networks and decision trees in the diagnosis of diabetes is based on the laws and properties of the compound.

The purpose of this study is to investigate the role and the scope of the application of data mining predictive science and medicine in a bid to build, evaluate, and the exploitation of data mining models in this regard. In this study to examine the related works in the field of prediction data mining in medicine, who recently published and is trying to highlight important issues and summarize methods and algorithms applied in the form of a series of training. According to the study of used in this study, in most cases to explore knowledge in the medical data from a combination of such as smart algorithms artificial neural network and the decision in the direction of the optimal - the former methods have been used.

Ant colony optimization is unique convergence of Swarm Intelligence and Bio-Inspired Artificial Intelligence. Ants are social insects with limited skills that live in colonies able to solve complex problems. The intelligence of the global society arises from self organization mechanisms, based on the indirect communication between individuals through pheromones. The Symmetric Travelling Salesman Problem here presented is a typical case that requires a self organization type of algorithm, in order to cope with the problem dynamics. The simulation results show how the ant colony optimization is able to solve the different possible routing cases. The innovation in this entire proposal (paper) is that an attempt has been made to improve the existing ant colony algorithm and then apply to the problem set. Ant colony optimization (ACO) has been successfully applied to solve combinatorial optimization problems, but it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence. These drawbacks are more evident when the problem size increases. In this paper, I have reported the analysis of using a reduced/lower pheromone trail bound and a dynamic updating rule for the heuristic parameters based on entropy to improve the efficiency of ACO in solving Traveling Salesman Problems (TSPs). TSPs are NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. From my simulations, it can be found that the proposed algorithm indeed has superior search performance over traditional ACO algorithms do.

Web-based decision support systems are used to model data and make adequate decisions based upon it. They address the needs of managers in solving structured and semi-structured problems. They have provided assistance to human discrimination to put right people in right places. This research paper focuses on Academic Course Scheduling Decision Support System, which provides a reliable tool that can be used to improve decision making process in academic course scheduling. Due to the dearth of adequate classrooms in most academic institutions, and pressure on the available ones, it becomes more and more difficult for school management to battle with the problem. This paper gives a model for appropriate scheduling of courses to classrooms, as well as determining courses priorities. To facilitate good implementation of this work, the researchers painstakingly took out time to collect enough data from the case study (Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria) as well as materials and journals from various authors/sites. The application was developed using a scripting language, PHP, and MySQL database as backend to effectively achieve the aims of this project.

- A cloud consists of several elements such as clients, cloudlets, datacenter, distributed servers etc. While Cloud computing is a term, which involves virtualization, distributed computing, networking and software and web services. Centrally, the issue is to establish an effective load balancing in cloud. There are two types of load balancing algorithms available one is Static and other is Dynamic. In this paper we have studied three different Static load balancing algorithms i.e. Round Robin, Throttled, and Optimal. To compare each algorithm we have prepared a set-up and implemented each algorithm. Finally, an analysis has been done about the three implemented algorithms to find the best algorithm in terms of overall response time of cloudlet and Datacenter Processing time of cloudlet.

Although electronic commerce has a lot of advantages it suffers from technological and non-technological limitations as well which have hindered its growth and acceptation. The imagination of insecurity and the lack of internationally accepted standards for quality, security and reliability are among the most important technological and non-technological obstacles facing electronic commerce. The insecure imagination of this technology has led to carrying out financial transactions by customers using traditional methods. Now, the question is raised that how the security of electronic financial transactions can be guaranteed for customers? Secure electronic transaction (SET) is one of the most important security protocols and guarantees the security of transactions of any kind. The present article explains the use of this protocol and the method of applying it in Telemedicine. Also, advantages and disadvantages of using SET will be discussed.

Data aggregation system is the majority sensible technique for wireless sensor networks. It reduce a large amount of communication. To screen announcement during aggregation homomorphism encryptions have been practical in previous be taught. So the enciphered data can be aggregated algebraically without decryption. Although data aggregation could considerably reduce broadcast, it is defenseless to some attacks. However, these schemes failed to satisfy multi-application surroundings. The main drawback of these scheme is that, it become insecure in case a number of sensor nodes are compromised. Also, these scheme do not provide secure counting. Thus, it is suffer due to unauthorized aggregation attacks. An alternative move toward for this problem is to collective encrypted messages in a straight line from SN, thereby avoid the forgery of aggregate result. To end these drawbacks, we propose a new hidden data aggregation system called CDAMA, provide CDA connecting multiple groups. It is extensive from homomorphism community encryption system. There are three contributions in the proposed system. First, it is intended a multi-application surroundings. Next, it mitigates the crash of compromising attack in single request environments. Compared with conservative schemes, CDAMA mitigates the collision of compromise SN from beginning to end the construction of manifold groups. It reduces the damage from illegal aggregations.