A New Content Based Search Mechanism for Image Retrieval Search Engine

In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% accuracy to retrieve the intended image by the user. But it is found that due to the efficiency of the applied algorithm, in all cases, the retrieved images are with the same accuracy irrespective of the input query image accuracy. This implementation is very much useful in the fields of forensic, defense and diagnostics system in medical field etc. .

TEP2MP: A text-emotion prediction model oriented to multi-participant text-conversation scenario with hybrid attention enhancement

<abstract> <p>With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an <italic>affective space mapping</italic> is constructed, which represents the original conversation-text as an n-dimensional <italic>affective vector</italic> so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.</p> </abstract>

DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing

This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

A Modified Cuckoo Search Algorithm and Its Applications in Function Optimization

A modified Cuckoo search algorithm (MCS) is proposed in this paper to improve the accuracy of the algorithm’s convergence by implementing random operators and adapt the adjustment mechanism of the Levy Flight search step length. Comparative experiments reveal that MCS can effectively adjust the search mechanism in the high-dimensional function optimization and converge to the optimal global value.

An Energy-Efficient One-Shot Scheduling Algorithm for Wireless Sensor Networks

In low-load wireless sensor networks, the power consumption of the node consists mainly of two parts: data transmission and node state switching. The lower node workload causes low energy consumption on data transmission, and the state switching energy of the node cannot be ignored. This paper proposes a one-shot time division multiple access (TMDA) scheduling with unlimited channels (SUC) on the assumption that the number of available channels is unlimited. SUC combines the receiver-based consecutive slot allocation with channel allocation, which minimises the number of node state switching and optimizes energy efficiency. Theoretical analysis demonstrates that the number of channels required by SUC does not exceed log 2 N + 1 , where N indicates the number of nodes. Seeing that the number of available wireless channels is limited in practice, the paper proposes the scheduling with limited channels (SLC) and uses a Lookahead Search mechanism to solve slot conflict. For the scalability of the algorithm, a distributed implementation based on the token change is proposed. The algorithm uses the depth-first-search (DFS) to pass the token to all nodes and terminates slot and channel assignment. The simulation results show our algorithm can reduce the energy consumption by minimizing the number of state switching and shorten the data aggregation time by reusing slots among nodes.

Optimal block search mechanism using deep recurrent neural network for enabling the code-efficiency in HEVC

Purpose High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications. Design/methodology/approach In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME. Findings The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827. Originality/value In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

Se-PKSE: Secure Public-Key Searchable Encryption for Cloud-Assisted Lightweight Platforms

Since more and more data from lightweight platforms like IoT devices are being outsourced to the cloud, the need to ensure privacy while retaining data usability is important. Encrypting documents before uploading to the cloud, ensures privacy but reduces data usability. Searchable encryption, specially public-key searchable encryption (PKSE), allows secure keyword search in the cloud over encrypted documents uploaded from IoT devices. However, most existing PKSE schemes focus on returning all the files that match the queried keyword, which is not practical. To achieve a secure, practical, and efficient keyword search, we design a dynamic ranked PKSE framework over encrypted cloud data named \textit{Secure Public-Key Searchable Encryption} (Se-PKSE). We leverage a partially homomorphically encrypted index tree structure that provides sub-linear ranked search capability and allows dynamic insertion/deletion of documents without the owner storing any document details. An interactive search mechanism is introduced between the user and the cloud to eliminate trapdoors from the search request to ensure search keyword privacy and forward privacy. Finally, we implement a prototype of Se-PKSE and test it in the Amazon EC2 for practicality using the RFC dataset. The comprehensive evaluation demonstrates that Se-PKSE is efficient and secure for practical deployment.

SMC protein RecN drives translocation and remodelling of RecA filament for homology search

While the molecular repertoire of the homologous recombination pathway is well studied, the search mechanism that enables recombination between distant homologous regions is poorly understood. Here, we follow the dynamics of the recombinase RecA, an essential component of homology search, after induction of a single double-strand break on the Caulobacter chromosome. We find that the RecA-nucleoprotein filament translocates in a directional manner in the cell, undergoing several pole-to-pole traversals, until homology search is complete. Simultaneously, the filament undergoes dynamic remodelling; both translocation and dynamic remodelling are contingent on the action of the SMC protein RecN via its ATPase cycle. We provide a stochastic description of RecN regulated changes in filament length during translocation via modulation of RecA assembly-disassembly. Together, the observed RecN driven RecA dynamics points to a novel optimal search strategy.