Informatics Publishing Limited

A. Nithya ¹, R. Satyabama ¹

Affiliations
1 Department of Electronics & Communication Engineering, Government College of Technology, Coimbatore, IN

Abstract

Carry Select Adder is known to be the fastest adder among the conventional adder structure. Due to rapidly growing mobile industry not only the faster arithmetic unit but also less area and low power arithmetic units are needed. The modified CSLA architecture has been developed by identifying the redundant logic operation and data dependence of the Conventional Carry Select Adder and Binary To Excess One Converter. In the proposed CSLA scheme the redundant logic operations present in the conventional CSLA are eliminated. Carry Selection operation is scheduled before the calculation of the final sum. Bit pattern of two anticipating Carry words (corresponding to cin=0 and 1) and fixed cin bits are used for logic optimization of Carry Select and generation unit. An efficient CSLA design is obtained using optimized logic unit. The proposed Carry Select Adder scheme is designed and implemented in cadence RC Encounter 180nm technology. Synthesis result shows that the proposed CSLA design involves 53% less area and consumes 50% less energy than Conventional CSLA and also the proposed CSLA design involves 20% less area and consumes 37% less energy than the BEC based CSLA on average, for different bit-widths.

Keywords

Redundant Logic, Data Dependence, Carry Select Adder (Csla), Binary to Excess One Converter (BEC).

Full Text

     

A. Nithya ¹, V. Sundaram ²

Affiliations
1 Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
2 MCA Department, Karpagam College of Engineering, Coimbatore, Tamil Nadu, IN

Abstract

Rice disease identification is one of the main issue of the country. The essence of the paper is identifying the rice disease in initial stage. This paper going to be create ready reckoned of the farmers. The main advantage of the paper is easily identifying the disease and gives the better solution for the farmers. It is the process which includes defining and redefining problems, formulating hypothesis, collecting, organizing and evaluating data; making deductions and reaching conclusions and at last presenting it in a detailed, accurate manner. This paper mainly focuses on concepts of data mining such as Classification, Decision Trees, and Neural Networks. A disease is an abnormal condition that injures the plant or causes it to function improperly. Diseases are readily recognized by their symptoms-associated visible changes in the plant. The organisms that cause diseases are known as pathogens. Many species of bacteria, fungus, nematode, virus and mycoplasma-like organisms cause diseases in rice. Disorders or abnormalities may also cause by abiotic factors such as low or high temperature beyond the limits for normal growth of rice, deficiency or excess of nutrients in the soil and water, pH and other soil conditions which affect the availability and uptake of nutrients, toxic substances such as H2S produced in soil, water stress and reduced light. However, here we will cover only the common diseases of rice those cause by pathogen.

Keywords

Decision Trees, Classification, Data Mining, Neural Network.

Full Text

     

A. Nithya ¹, B. Ramakrishnan ², Resul Das ³

Affiliations
1 S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR

Abstract

Cloud computing is the mass storage area that helps the user to access the data anywhere. There are so many platforms provided by the cloud service provider. They are SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service) etc. Though security is not fully provided by the cloud service provider to reshape the advances in information technology, cloud computing is expected as an updated technology. The data was securely stored in the cloud and if it is corrupted then the proxy is implemented to regenerate the corrupted data in the cloud. Thus security and integrity is successfully achieved. This is further extended by implementing efficient file fetching by the third party user. To maintain efficient file fetching system Multi authority cloud model is proposed. The model is continuing with the proposed entities such as Attribute authority (AA), Certificate Authority (CA) and Third party end user. The data is encrypted by the owner and stored in the cloud server. CA is used to delegate the Secret Key (SK) to AA and Public Kay (PK) to user. After Checking the authentication of the owner CA provides PK to the owner only then the owner is allowed to upload the data in cloud, the data is encrypted and outsource to the cloud server. Using SK the third party user is allowed to view the data from the cloud. If the user enter the wrong key or misuse the data, user will be revoked. If the User needs to download or update or delete the data in the cloud the user need to send a Data Access Privilege (DAP) request to the respective owner. Certificate authority is responsible to generate a key to the entities such as User, Data Owner and attributes.

Keywords

Cloud Computing, Security, Third Party Auditor (TPA), Proxy, RSA Algorithm, Regeneration, Multiuser Authentication.

Full Text

   

B. K. Rakesh ¹, T. G. Abhay ², Akhil Baby ¹, A. Nithya ¹, G. Gokul Krishna ¹, M. Gurumurthy ¹

Affiliations
1 Department of Mechanical, Jyothy Institute of Technology, Tataguni, Bangalore-560082, IN
2 Department of Mechanical, Jyothy Institute of Technology, Tataguni, Bangalore-560062, IN

Abstract

The conventional automobile air conditioning system draws power from the engine. This project aims at introducing a turbo charger in order to transform the kinetic energy of the exhaust gas in to useful power to run the compressor of the AC system. This avoids the extraction of power from the engine.

Keywords

Exhaust Gas, Turbocharger, Generation of Rotary Motion from Exhaust, Exhaust Energy Recovery.

Full Text

   

G. Purusothaman ¹, A. Nithya ²

Affiliations
1 Rathnavel Subramaniam College of Arts and Science, Sulur, Coimbatore - 402, Tamil Nadu, IN
2 Department of CS, Rathnavel Subramaniam College of Arts and Science, Sulur, Coimbatore - 402, Tamil Nadu, IN

Abstract

Heart disease is the frequently found disease in various peoples which would cause more serious and dangerous effects. Various studies have been projected earlier whose major aim is to predict the heart disease more accurately. In our previous research method Fuzzy Rough Set Theory combined with Support Vector Machine (FRS - SVM) is introduced which can ensures the optimal prediction rate by selecting the risk factors accurately which can lead to improved accuracy rate. However FRS-SVM might lack in its performance in case of presence of more missing values in the database. This research method cannot support the large dimensional dataset which needs to be focused well enough for accurate prediction rate. This problem is resolved in this investigation by introducing the framework namely Heart disease prediction using Alpha Rough Set Theory combined with Fuzzy SVM (ARST-FSVM). In this research method, Modified K-Means clustering algorithm is utilized for preprocessing the input dataset which would avoid the noisy data present in the database. Then missing data value in the database is handled using normalization technique where NLLS imputation is applied. And then feature dimensionality reduction is done using Alpha rough set theory (α-RST) approach. From those reduced feature set, optimal feature selection in terms of relevancy is done using Hybrid Bee colony algorithm with Glowworm Swarm Optimization (HBC-GSO) approach. Finally heart disease prediction is done using classifier namely fuzzy based SVM. The overall research method ensures that the proposed research technique leads to ensure it can direct to most favorable and accurate heart disease diagnosis outcome.

Keywords

Large Data Set, Heart Disease Prediction, Missing Values, Accurate Observation, Feature Reduction.

Full Text

     

L. Krishnakumar ¹, A. Nithya ¹

Affiliations
1 School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, Coimbatore, IN

Abstract

Cloud computing technology is the scope of next generation intelligent computing which combines everything into one. In Cloud with a vast verity of users from heterogeneous systems calls the cloud services at a single instance. So there must be a way in which all the resources are made available to the requesting user in a good manner to satisfy their needs. The mechanism of allocating available resources to the needed cloud applications through the internet is called Resource Allocation.

Evaluation of social media and data science makes the feature the data centric instead of knowledge centric. When Resource Allocation is done in conjunction with user annotations as introduced in this paper can reduce the cost incurred for a user and hence it can attract more cloud users in future. User annotations like cost, deadline can be used. Users are allowed to submit the parameters during job submission. The user inserted parameters will then be considered while allocating resources to them. Big data analytics with machine learning helps to optimize the cost, the main purpose of this paper is to increase information sharing among cloud users and cloud providers and provide benefits to users in terms of the long term user with annotated parameters.

Keywords

Big Data, Data Analytics, Resource Allocation, Virtual Machine, User Annotation.

Full Text

     

S. Dhivya ¹, A. Nithya ¹, T. Abirami ¹

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur − 639113, Tamil Nadu, IN

Abstract

Objective: To make early diagnosis of breast cancer from mammogram image. Methods/Statistical analysis: To avoid the misdiagnosis, we proposed a system to sort the suspicious masses from the mammogram image by using Extreme learning machine algorithm. The ELM based classifier is used to classify the input data as malignant and benign classes with the abnormal class. The effectiveness of the ELM algorithm is superior to the other existing algorithms for mammogram classification problems with its reduced training time and classification accuracy. Findings: We provide an optimistic method for binary class classification of mammograms using extreme learning machine algorithm. Mammography is a technique which is preferred for early diagnosis of breast cancer. On the other hand, in most cases, it is not easy to differentiate benign and malignant tumor without biopsy, hence misdiagnosis is always possible. The machine learning algorithm provides high accuracy than other techniques and also the execution time is very low when compared to normal diagnosis. The existing methods are very slow compared to this proposed technique. The input images are the mammogram image and the segmentation, preprocessing is performed to remove the noises present. Application/Improvements: The main application of the system is the early diagnosis of cancerous cell present and also classifies the normal and abnormal images.

Keywords

Breast Cancer, Extreme Learning Machine, Mammography

Full Text

   

A. Nithya ¹, S. Dhivya ¹, T. Abirami ¹

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, IN

Abstract

Objective: A brand new version of RSA, known as Multi Key Generation Scheme generation scheme, has been proposed. For sensitive information, our schemes will be a useful resource in replacing the records among cloud to IoT and IoT devices. Whilst cryptography belongs to the asymmetric type, then it has public and personal keys. Methods/Statistical Analysis: For reminiscence performance, our scheme reuses the RSA scheme with a Diophantine shape of the nonlinear equation. Furthermore, our scheme overall performances well and this is especially because of the use of RSA public key. Findings: MKGS does not require multiplicative inverse function or prolonged Euclid’s algorithm. Finally, we've made an experimental result on diverse stages of MKGS P.C such as key generation, encryption, and decryption via varying the N-bit modulo bits from 1K to 10K. Application/Improvements: We can develop new features rather than using Modulation bits.

Keywords

Cloud computing, Cryptography, Multi Key, Public Key Cryptosystem, IoT, RSA

Full Text