ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

In today�s world, online shopping is very attractive and grown exponentially due to revolution in digitization. It is a crucial demand to provide recommendation for all the search engine to identify users� need. In this paper, we have proposed a ANOVA Cosine Similarity Image Recommendation (ACSIR) framework for vertical image search where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p value by considering image visual features on text-based search. Expanded queries are generated for user input query, and text-based search is performed to get the initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain-specific site. Experiment results show that the ACSIR outperforms iLike method by providing more relevant products to the user input query. © 2017, Springer-Verlag London

Ir_urfs_vf: Image Recommendation with User Relevance Feedback Session and Visual Features in Vertical Image Search

In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query

Image Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images

EDOCR: ENERGY DENSITY ON-DEMAND CLUSTER ROUTING IN WIRELESS SENSOR NETWORKS

Energy management is one of the critical parameters in Wireless Sensor Networks. In this paper we attempt for a solution to balance the energy usage for maximizing the network lifetime, increase the packet delivery ratio and throughput. Our proposed algorithm is based on Energy Density of the clusters in Wireless Sensor Networks. The cluster head is selected using two step method and on-demand routing approach to calculate the balanced energy shortest path from source to sink. This unique approach maintains the balanced energy utilization among all nodes by selecting the different cluster heads dynamically. Our simulation results have compared with one of the plain routing scheme (EBRP) and cluster based routing (TSCHS), which shows the significant improvements in minimizing the delay and energy utilization and maximizing the network lifetime and throughput with respect to these works

IRAbMC: Image Recommendation with Absorbing Markov Chain

Image Recommendation is an important feature for search engine as tremendous amount images are available online. It is necessary to retrieve relevant images to meet user's requirement. In this paper, we present an algorithm Image Recommendation with Absorbing Markov Chain (IRAbMC) to retrieve relevant images for user input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Absorbing Markov chain is used to calculate keyword relevance. Experiments results show that the IRAbMC algorithm outperforms Markovian Semantic Indexing (MSI) method with improved relevance score of retrieved ranked images

Efficient Retransmission QoS-Aware MAC Scheme in Wireless Sensor Networks

In this paper, an Efficient Retransmission Random Access Protocol (ERRAP) is designed that combines scheme of collision avoidance and energy management for low-cost, short-range wireless radios and low-energy sensor nodes applications. This protocol focuses on efficient Media Access Control (MAC) schemes to provide autonomous Quality of Service (QoS) to the sensor nodes in one-hop QoS retransmission group in WSNs where the source nodes do not have receiver circuits. These sensor nodes can only transmit data to a destination node, but cannot receive acknowledgement or control signals from the destination node. The proposed scheme ERRAP provides QoS to the nodes which work independently on predefined time by allowing them to transmit each packet an optimal number of times within a given period. Our simulation results demonstrate the superiority of ERRAP scheme which increases the delivery probability and reduces the energy consumption

LR3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

Existing reliable-oriented routing protocols computes link reliability based on the packet reception ratio and neglects impact of various parameters such as noise, shadowing, battery-lifespan, uncertainty and geographic locations. In this paper, we propose a Link Reliable Reactive Routing (LR3) protocol for WSNs to accomplish reliable and resilience to out-of-order transmission and path diversity at each hop. The log-normal shadowing model is used to estimate link reliability and a back-off scheme is used to determine delay. A new cost estimated to? nd forwarding nodes on mentor path that includes link reliability, delay, status of queue at forwarding node and packet advancement at the forwarding node. LR3 is simulated using NS-2 and results show that it outperforms other reactive routing protocols in terms of packet delivery ratio, latency, link reliability and data transmission cost [1][2]

[PDF] from arxiv.org QoS group based optimal retransmission medium access protocol for wireless sensor networks

This paper presents, a Group Based Optimal Retransmission Medium Access (GORMA) Protocol is designed that combines protocol of Collision Avoidance (CA) and energy management for low-cost, short-range, low-data rate and low-energy sensor nodes applications in environment monitoring, agriculture, industrial plants etc. In this paper, the GORMA protocol focuses on efficient MAC protocol to provide autonomous Quality of Service (QoS) to the sensor nodes in one-hop QoS retransmission group and two QoS groups in WSNs where the source nodes do not have receiver circuits. Hence, they can only transmit data to a sink node, but cannot receive acknowledgement control signals from the sink node. The proposed protocol GORMA provides QoS to the nodes which work independently on predefined time by allowing them to transmit each packet an optimal number of times within a given period. Our simulation results shows that the performance of GORMA protocol, which maximize the delivery probability of one-hop QoS group and two QoS groups and minimize the energy consumption

MSIGT: Most Significant Index Generation Technique for cloud environment

Cloud Computing is a computing paradigm for delivering computational power, storage and applications as services via Internet on a pay-as-you-go basis to consumers. The data owner outsources local data to the public cloud server to reduce the cost of the data management. Critical data has to be encrypted to ensure privacy before outsourcing. The state-of-the-art SSE schemes search only over encrypted data through keywords, hence they do not provide effective data utilisation for large dataset files in cloud. We propose a Most Significant Index Generation Technique (MSIGT), that supports secure and efficient index generation time using a Most Significant Digit (MSD) radix sort. MSD radix sort is simple and faster in sorting array strings. A mathematical model is developed to encrypt the indexed keywords for secure index generation without the overhead of learning from the attacker/cloud provider. It is seen that the MSIGT scheme can reduce the cost of data on owner side to O(NT × 3) with a score calculation of O(NT). The proposed scheme is effective and efficient in comparison with the existing algorithms