Increase Hit Rate Research Articles

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.

Predicting the outcome of biological assays based on high-throughput imaging data is a highly promising task in drug discovery since it can tremendously increase hit rates and suggest novel chemical scaffolds. However, end-to-end learning with convolutional neural networks (CNNs) has not been assessed for the task biological assay prediction despite the success of these networks at visual recognition. We compared several CNNs trained directly on high-throughput imaging data to a) CNNs trained on cell-centric crops and to b) the current state-of-the-art: fully connected networks trained on precalculated morphological cell features. The comparison was performed on the Cell Painting data set, the largest publicly available data set of microscopic images of cells with approximately 30,000 compound treatments. We found that CNNs perform significantly better at predicting the outcome of assays than fully connected networks operating on precomputed morphological features of cells. Surprisingly, the best performing method could predict 32% of the 209 biological assays at high predictive performance (AUC > 0.9) indicating that the cell morphology changes contain a large amount of information about compound activities. Our results suggest that many biological assays could be replaced by high-throughput imaging together with convolutional neural networks and that the costly cell segmentation and feature extraction step can be replaced by convolutional neural networks.

A Per-Face Popularity Scheme to Increase Cache Robustness in Information-Centric Networks

In-network storage is an essential element of nearly all Information-Centric Networking (ICN) architectures. Thus, protecting the cache of popular items in the network storage devices is crucial. With cache pollution attacks, an attacker seeks to push legitimately popular items out of the cache in favor of less popular items. This paper presents a per-face popularity (PFP) scheme to mitigate effects of cache pollution attacks and to increase overall cache robustness in Information-Centric Networks. The scheme normalizes the contributions from each face so that Interests arriving over any one face has no more effect on the popularity than any other face. As a result, the items that remain in the cache are those that have legitimate popularity, represented across all faces. The experimental results confirm its effectiveness against cache pollution attacks over one or a relatively small percentage of the faces, and demonstrate its overall effectiveness in preserving popular items and increasing hit rate.

A Hybrid Virtual Screening Protocol Based on Binding Mode Similarity.

In structure-based virtual screening (SBVS), a scoring function is usually applied to rank a database of docked compounds. Docking programs are often successful in reproducing experimental binding modes; however, the estimation of binding affinity still is the Achilles' heel of docking. The integration of SB and ligand-based (LB) methods is considered a promising strategy to increase hit rates in VS. Herein, we describe a hybrid protocol that is based on the assessment of binding mode similarity between docked compounds and a bound reference ligand. In this context, both experimental and computationally modeled poses have been successfully used as references for three-dimensional (3D) similarity calculations. In this chapter, the methods applied in recent validation studies are described.

Realisation of Cache Optimisation using New Technique

An impressive design of cache memory is an important view in computer architecture to boost or increase the system performance. In this task we study the response of reducing the cache performance to the cache address on the performance experimentally. Since cache miss penalty has direct contact on the system’s performance. To overtake from this, we proposed a new model in which we use revisited second chance FIFO (RS FIFO), which uses partial comparator and use multiple search to increase cache hit rate, by reducing cache miss rate. The Above proposed technique gives maximum hit rate when using revisited SFIFO rather than FIFO. Keywords: cache mapping technique; hit rate; miss rate; cache optimisation; cache replacement policies.

HitBand: A Prefetching Model to Increase Hit Rate and Reduce Bandwidth Consumption

HitBand: A Prefetching Model to Increase Hit Rate and Reduce Bandwidth Consumption

Complexity of sales situation and sales lead performance: An empirical study in business-to-business company

Close collaboration in business-to-business relationships has been an interest of both researchers and managers; however, companies are often struggling to obtain the benefits of close collaboration with their customers. Our study examines the complexity of the sales situation from three perspectives 1) relational complexity, 2) complexity of internal sales tasks, and 3) vendor's cross-business unit collaboration, and its effect on the sales lead performance in a business-to-business context. We adopted a mixed methods approach; our data consists of interviews with sales personnel and a Customer Relationship Management (CRM) system data of 4000 sales leads from a large IT company that sells integrated solutions. Sales leads are classified into three categories: win, lost, and canceled. Based on multinomial logistic regression analysis, the results show that close collaboration with customers increases the probability of sales leads to be canceled. Our findings suggest that the selling approach focusing on close collaboration with the customers may not be a method to increase hit rates, due to possible high amount of canceled sales leads. This should be considered when salespeople's performance is measured in highly collaborative business-to-business environment. We conclude our paper with managerial and theoretical implications, and avenues for the further research.

Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks

Abstract The big data revolution happening in and around 21st century has found a resonance with banking firms, considering the valuable data they’ve been storing since many decades. This data has now unlocked secrets of money movements, helped prevent major disasters and thefts and understand consumer behaviour. Banks reap the most benefits from big data as they now can extract good information quickly and easily from their data and convert it into meaningful benefits for themselves and their customers. Banks internationally are beginning to harness the power of data in order to derive utility across various spheres of their functioning, ranging from sentiment analysis, product cross selling, regulatory compliances management, reputational risk management, financial crime management and much more. Indian banks are catching up with their international counterparts; however a lot of scope remains. This paper aims to capture how big data analytics is being successfully used in banking sector, with respect to following aspects: 1. Spending pattern of customers 2. Channel usages 3. Customer Segmentation and Profiling 4. Product Cross Selling based on the profiling to increase hit rate 5. Sentiment and feedback analysis 6. Security and fraud management The data used is secondary data from a bank while the analysis is of primary nature. This study reveals some of the best practices being adopted by banks globally, and can be replicated by Indian banks to enhance their financial service offerings to customers.

Identification of Novel Antimalarial Chemotypes via Chemoinformatic Compound Selection Methods for a High-Throughput Screening Program against the Novel Malarial Target, PfNDH2: Increasing Hit Rate via Virtual Screening Methods

Malaria is responsible for approximately 1 million deaths annually; thus, continued efforts to discover new antimalarials are required. A HTS screen was established to identify novel inhibitors of the parasite's mitochondrial enzyme NADH:quinone oxidoreductase (PfNDH2). On the basis of only one known inhibitor of this enzyme, the challenge was to discover novel inhibitors of PfNDH2 with diverse chemical scaffolds. To this end, using a range of ligand-based chemoinformatics methods, ∼17000 compounds were selected from a commercial library of ∼750000 compounds. Forty-eight compounds were identified with PfNDH2 enzyme inhibition IC50 values ranging from 100 nM to 40 μM and also displayed exciting whole cell antimalarial activity. These novel inhibitors were identified through sampling 16% of the available chemical space, while only screening 2% of the library. This study confirms the added value of using multiple ligand-based chemoinformatic approaches and has successfully identified novel distinct chemotypes primed for development as new agents against malaria.

Combined SVM-based and docking-based virtual screening for retrieving novel inhibitors of c-Met

Aberrant c-Met activation has been demonstrated to be implicated in tumorigenesis and anti-cancer drug resistance. Discovery of c-Met inhibitors has attracted much attention in recent years. In this study, a support vector machine (SVM) classification model that discriminates c-Met inhibitors and non-inhibitors was first developed. Evaluation through screening a test set indicates that combined SVM-based and docking-based virtual screening (SB/DB-VS) considerably increases hit rate and enrichment factor compared with the individual methods. Thus the combined SB/DB-VS approach was adopted to screen PubChem, Specs, and Enamine for c-Met inhibitors. 75 compounds were selected for in vitro assays. Eight compounds display a good inhibitory potency against c-Met. Five of them are found to have novel scaffolds, implying a good potential for further chemical modification.

An Improved Weighted-Residue Profile Based Method of Using Protein-Ligand Interaction Information in Increasing Hits Selection from Virtual Screening: A Study on Virtual Screening of Human GPCR A2A Receptor Antagonists.

The use of protein-ligand interaction information has been reported to improve and optimize the docking results in virtual screening experiments. Here we propose an improved weighted-residue profile based method to profile the protein-ligand interactions based on the available dataset of known actives and utilize this weighted residue profile information, together with the scoring function, as selection criteria to increase hit rates in virtual screening experiments. The generated fingerprint data is not directly based on the protein-ligand complexes but taken from the available interaction data derived from the docking results. The ability of the method to recover the active compounds was tested on two data sets of a compound library designed for antagonists of the A2A receptor. The results show better hits enrichments by using the weighted-residue based profiles of protein-ligand interactions as compared to the normal binding energy based scoring schemes of the two docking programs.

Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering

We have investigated the influence of post-filtering virtual screening results, with pharmacophoric features generated from an X-ray structure, on enrichment rates. This was performed using three docking softwares, zdock+, Surflex and FRED, as virtual screening tools and pharmacophores generated in UNITY from co-crystallized complexes. Sets of known actives along with 9997 pharmaceutically relevant decoy compounds were docked against six chemically diverse protein targets namely CDK2, COX2, ERα, fXa, MMP3, and NA. To try to overcome the inherent limitations of the well-known docking problem, we generated multiple poses for each compound. The compounds were first ranked according to their scores alone and enrichment rates were calculated using only the top scoring pose of each compound. Subsequently, all poses for each compound were passed through the different pharmacophores generated from co-crystallized complexes and the enrichment factors were re-calculated based on the top-scoring passing pose of each compound. Post-filtering with a pharmacophore generated from only one X-ray complex was shown to increase enrichment rates in all investigated targets compared to docking alone. This indicates that this is a general method, which works for diverse targets and different docking softwares.

Comparison of Methods for Sequential Screening of Large Compound Sets

Sequential screening is an iterative procedure that can greatly increase hit rates over random screening or non-iterative procedures. We studied the effects of three factors on enrichment rates: the method used to rank compounds, the molecular descriptor set and the selection of initial training set. The primary factor influencing recovery rates was the method of selecting the initial training set. Rates for recovering active compounds were substantially lower with the diverse training sets than they were with training sets selected by other methods. Because structure-activity information is incrementally enhanced in intermediate training sets, sequential screening provides significant improvement in the average rate of recovery of active compounds when compared with non-iterative selection procedures.

Performance of the Continuous Performance Test among community samples.

The Continuous Performance Test (CPT) has been modified to be used widely as a potential vulnerability marker of schizophrenia. In genetic analyses of schizophrenia, well-established norms for the CPT are essential in choosing a suitable threshold of CPT for classifying subjects as affected or unaffected. In this study, we investigated the performance of 115 adolescents and 345 adults, randomly sampled from a community, on two sessions of the CPT 1-9 (undegraded and 25% degraded). The results showed that an older age was associated with a decreasing hit rate and sensitivity (d'), while a higher level of education was associated with an increasing hit rate and d' for both sessions of the CPT. Men had higher hit rates and d' than women for the degraded CPT. A practice effect during the second session of the CPT was noted among 20 to 33 percent of the subjects. Poorer CPT performance was associated with schizotypy measured by the Perceptual Aberration Scale and the Schizotypal Personality Questionnaire. Thus, in assessing CPT performance among schizophrenia patients or high-risk populations, we must compare their results to age-, education-, and sex-corresponding norms. The data presented in this report will be valuable in this regard.

Using name-based mappings to increase hit rates

Clusters of identical intermediate servers are often created to improve availability and robustness in many domains. The use of proxy servers for the World Wide Web (WWW) and of rendezvous points in multicast routing are two such situations. However, this approach can be inefficient if identical requests are received and processed by multiple servers. We present an analysis of this problem, and develop a method called the highest random weight (HRW) mapping that eliminates these difficulties. Given an object name and a set of servers, HRW maps a request to a server using the object name, rather than any a priori knowledge of server states. Since HRW always maps a given object name to the same server within a given cluster, it may be used locally at client sites to achieve consensus on object-server mappings. We present an analysis of HRW and validate it with simulation results showing that it gives faster service times than traditional request allocation schemes such as round-robin or least-loaded, and adapts well to changes in the set of servers. HRW is particularly applicable to domains in which there are a large number of requestable objects, there is a significant probability that a requested object will be requested again, and the CPU load due to any single object can be handled by a single server. HRW has now been adopted by the multicast routing protocols PIMv2 and CBTv2 as its mechanism for routers to identify rendezvous points/cores.