A Survey on Flip Flop Replacement to Latch on Various Design

This paper presents survey for the replacement of flip flop to latches and the advantages of the latch based sequential design Flip flop are the major part of the design a sequential elements and this flip flop has more disadvantages as performance decreases and area increases. An alternate method to increase the performance and reduce the area size latches. Latches are used instead of flip flops in certain places to increase the performance and decrease the area

A User Query-Centered Recommender System from Public Repository

Query-based User Information Categorization and Extraction (QICE) methods allow the classical query extraction with its knowledge obtained from useful resources. Open Data encodes machine-readable user facts from different sources, including third-party, that play a vital role in this QICE. Mining techniques from documents available in free sources and constructing the user text based on the user query with its knowledge and analysis are the core research problems in the public repositories and query understanding tasks such as query pattern analysis and user information requirements. However, the public repositories encode the user information through Wikipedia and web pages which are static, and these do not understand the user requirement perspectives. These static web pages have many quality issues with user query information, such as information extracted, complete data representing, time of query retrieval, and correctness of the information categorized based on the user query. QICE methods are, therefore, facing problem user query variances and type of user query confusability. In this paper, a query recommender system proposes developing a technique for user-query-centered knowledgeable integration and addressing the challenges of knowledge mining of the Twitter social network by extracting the knowledge from query log data. The proposed User-Query Centered Recommender System (UQCRS) is applied to exploit different measures to demonstrate the efficiency of recommendations delivered. The proposed algorithm exhibits an effective result to the search shortcuts issues. And to provide a performance comparison of the proposed system, the comprehensive evaluation is compared with well-known methods and demonstrates the impact of the results

A Survey on Flip Flop Replacement to Latch on Various Design

This paper presents survey for the replacement of flip flop to latches and the advantages of the latch based sequential design Flip flop are the major part of the design a sequential elements and this flip flop has more disadvantages as performance decreases and area increases. An alternate method to increase the performance and reduce the area size latches. Latches are used instead of flip flops in certain places to increase the performance and decrease the area

Optimized Ensemble Approach for Multi-model Event Detection in Big data

Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately

An Framework for Simple Sequence Repeat Reduction with Information Extraction using Machine Learning

Demand for the agricultural improvements using the advanced computer algorithms have increased in the recent years. The primary focus is on the higher crop production rates with least damage to the crops due to various diseases. In the recent times, a good number of research attempts are observed to formulate multiple computerized algorithms to identify the Amino Acid sequence and further protein sequences, which are responsible for diseases to the plants and crops. However, due to the higher complexity of DNA structure and further the complex process for DNA to Amino Acid extraction, these recent researches have produced unsatisfactory outcomes.  Henceforth, in order to solve the primary challenge of higher time complexity of the DNA processing methods, this work proposes two algorithms to reduce the DNA sequence length without losing vital information using machine learning. Firstly, the use of clustering method to reduce the size ensures least information loss and best processing time. Secondly, the look up based indexed Amino Acid extraction process ensures higher correctness of the extraction and again in best possible time. The proposed framework produced nearly 98% accuracy in 0.107 sec time frame, which is relatively 5% improvement in accuracy and 10% improvement in time complexit

A Review on Software Performance Analysis for Early Detection of Latent Faults in Design Models

Organizations and society could face major breakdown if IT strategies do not comply with performance requirements. This is more so in the era of globalization and emergence of technologies caused more issues. Software design models might have latent and potential issues that affect performance of software. Often performance is the neglected area in the industry. Identifying performance issues in the design phase can save time, money and effort. Software engineers need to know the performance requirements so as to ensure quality software to be developed. Software performance engineering a quantitative approach for building software systems that can meet performance requirements. There are many design models based on UML, Petri Nets and Product-Forms. These models can be used to derive performance models that make use of LQN, MSC, QNM and so on. The design models are to be mapped to performance models in order to predict performance of system early and render valuable feedback for improving quality of the system. Due to emerging distributed technologies such as EJB, CORBA, DCOM and SOA applications became very complex with collaboration with other software. The component based software systems, software systems that are embedded, distributed likely need more systematic performance models that can leverage the quality of such systems. Towards this end many techniques came into existence. This paper throws light into software performance analysis and its present state-of-the-art. It reviews different design models and performance models that provide valuable insights to make well informed decisions

Identity-Based Cryptosystem Based on Tate Pairing

Tate Pairings on Elliptic curve Cryptography are important because they can be used to build efficient Identity-Based Cryptosystems as well as their implementation essentially determines the efficiency of cryptosystems In this work we propose an identity-based encryption based on Tate Pairing on an elliptic curve The scheme was chosen ciphertext security in the random oracle model assuming a variant of computational problem Diffie-Hellman This paper provides precise definitions to encryption schemes based on identity it studies the construction of the underlying ground field their extension to enhance the finite field arithmetic and presents a technique to accelerate the time feeding in Tate pairing algorith

Beneficiation studies on beach placer sample for steel making industries

Beneficiation studies were carried out on the Talashil beach placer sample of South Maharastra Coast, India. The sample contains magnetite, ilmenite, rutile, hematite, goethite and chromite as opaque minerals in the sample. The total heavy minerals fraction reaches 53.8 % by weight whereas the total magnetic minerals are 56.9%. It is observed that the 2nd stage DHIMS magnetic fraction contains 65.2 % Fe2O3 with an over all yield of 37.8 % and a 86 % recovery from a containing 26.8 % Fe2O3 feed. This product can be used in the pellet feed for steel making after suitable blending with high-grade iron ore fines