Among the factors argued to contribute to a bilingual advantage in executive function (EF), the combination of languages spoken by the bilingual is often overlooked. In this study, we explored the role of language similarity on memory and EF task by comparing performance of three groups of young adults—Hungarian–Serbian and Slovak–Serbian early balanced bilinguals, and Serbian-speaking monolinguals. Slovak is typologically related to Serbian, which are both Slavic, in contrast to Hungarian, which is Finno–Ugric. On the computerized tasks from the CANTAB battery , differences between monolinguals and bilinguals emerged on the EF tasks: Stockings of Cambridge (SOC) and Attentional Set Shifting (AST), but not the memory tasks: Delayed Matching to Sample (DMS), Paired Associate Learning (PAL), Spatial Working Memory (SWM). Both Hungarian–Serbian and Slovak–Serbian bilinguals outperformed the monolinguals on the more difficult SOC tasks, solved using more than a minimally required number of moves. This is in line with reports that bilinguals perform better under more complex conditions that require more monitoring and switching. However, bilinguals speaking Hungarian and Serbian spent less time preparing to execute the simpler SOC tasks, which can be solved in a minimum of two or three moves; they also exhibited reduced local switching cost and were faster overall on AST than both the Slovak–Serbian bilinguals and Serbian monolinguals. The advantageous performance of speakers of the typologically unrelated languages in our study suggests that these bilinguals may have more efficient attention switching and inhibition systems than bilinguals who speak typologically similar languages.The online version contains supplementary material available at 10.3758/s13421-022-01345-8. A large body of literature reports advantages for bilingual speakers over monolinguals in tasks that tap into different domains of cognitive abilities , and speakers of two typologically distant languages, Serbian and Hungarian , who will be compared with monolingual Serbian speakers. In line with suggestions from recent reviews of the literature  were slightly older than the monolinguals , t(66.266) = 3.6199, p = .0006, but the bilingual groups did not differ in age, t(34.37) = .38284, p = .7042 .Our participants were all non–immigrant nationals of Serbia, residing in Novi Sad, the capital of the Serbian province of Vojvodina, at the time of testing. All bilingualt tests showed no significant difference between the two bilingual groups on any of the four BLP dimensions, either with respect to the minority language or to the majority language. Our two bilingual groups did not differ in terms of language use or proficiency, nor language exposure: Both groups acquired their home languages from birth or before the age of 3 .For our monolingual group, the recruitment criteria required participants to have lived all their lives in Vojvodina/Serbia, and to have been schooled in Serbian, without being exposed to another home language .The study was approved by the Ethics Boards of the University of Novi Sad and University College London. The current experiments were carried out as a part of a larger study on language and cognition in typically and atypically developing Serbian speakers. Written informed consent was obtained from all the participants. Participants were tested on a series of computerized nonverbal tasks that tap into memory and EF from the Cambridge Neuropsychological Test Automated Battery  in the course of two sessions. In the first session, they were administered the language background questionnaire and two of the CANTAB tasks: Delayed Matching to Sample (DSM) and Stockings of Cambridge (SOC). The remaining tasks were administered in the second session: Paired Associates Learning (PAL), Spatial Working Memory (SWM), and Attention Shifting Task (AST). For organizational reasons we were not able to administer all the tests to each monolingual speaker but in line with the standard literature Harris, , we randDelayed Matching to Sample (DMS) taps into immediate and delayed visual matching. At each trial, the participant is briefly presented with a sample visual pattern at the top of the screen and instructed to indicate from the four choice patterns presented at the bottom of the screen the one which is identical to the sample. The four choice patterns are presented either while the target is still present on the screen, immediately following the target presentation, or after a delay of 4 or 12 seconds. In addition to testing perceptual abilities, the delayed conditions also test the capability of maintaining the visual information in memory. The measure of performance is the proportion of choosing the correct pattern on the first attempt in each of the delay conditions.Paired Associates Learning (PAL) tests episodic memory—namely, the ability to bind various information into a single episode. In this case, the binding is to be performed on visual and spatial information, as the identity of the visual object needs to be paired with its location. On each trial, a certain number of boxes are presented on the screen, which open one at a time and reveal whether they contain a hidden visual pattern. The task is to remember the location of each hidden pattern and to tap the box that reveals the pattern upon presentation. The successful attempt leads to the next stage which contains more patterns and/or more boxes, whereas the failed attempt is followed by a repeated presentation. The cycle is repeated either until the task is accomplished, or the test is aborted. The core performance measure is the first trial memory score .Spatial Working Memory (SWM) addresses two working memory domains, the short-term storage and the updating of information . Given the self-ordered nature of the task, an additional measure of executive functioning can be calculated. This strategy measure represents the number of specific boxes used to begin the search for a token. Strategy is calculated as the number of different boxes used to start the new search, and the higher number reflects the lack of strategy .Stockings of Cambridge (SOC), based on the Tower of London task . The CANTAB manual suggests that the initial thinking time measure may not be sensitive enough to assess high functioning populations such as healthy adults . We believed that this measure would be more sensitive to variation amongst the student population known for ceiling performance on similar tasks . The measure typically obtained from this task is  Grundy, .Attention Shifting Task (AST) taps the executive function of shifting attention in the presence of visual cues. Each trial consists of an arrow presented on one side of the screen and the written reminder of the current task, which is presented on the top of the screen. The task for the participant is either to indicate the side of the screen on which the arrow is presented or the direction in which the arrow is pointing. The side and the direction are sometimes congruent , and sometimes incongruent . Among the assessed blocks, there are two single-task blocks (the side of the screen only and the direction of the arrow only), and one mixed block in which the two tasks switch randomly. The order of the blocks is fixed across participants. The typically assessed measures are: mean correct reaction time ; congruence cost, calculated as the difference between RT to congruent trials and to incongruent trials ; global switch cost, the difference in processing time observed in single-task blocks (without the task switching) and nonwitch trials in a mixed block ; and finally, local switch cost, the difference between the RT observed in switch trials of the mixed block and in nonswitch trials of the mixed block  and prechoices delay  were included as the predictors of the first choice accuracy . As presented in Table The proportion of correct first choice responses is presented in Fig. Hungarian, Slovak, none) and number of stimuli that was to be learned  were included as predictors (fixed effects) of accuracy in the first attempt . The results presented in Table The proportion of the correct responses in the first attempt for the assessed trials in this task is presented in Fig. ed 1, 2, , 6, 8 wethe number of between errors, which is the number of errors made by revisiting the boxes that had been cleared previously . The participant who used fewer boxes when initializing the search was considered more successful in approaching the task. No significant difference between the strategy scores of monolinguals and bilinguals was observed: neither for Hungarian–Serbian bilinguals, U = 324.500, Z = .202, p = .841 , nor for Slovak–Serbian bilinguals, U = 247.500, Z = 1.091, p = .272 . Similarly, no such difference was found for the two bilingual groups either: U = 169.000, Z = .813, p = .416 .The second measure assessed was Figure The results of the generalized linear mixed effect regression with language distance number of minimum moves (required to solve the problem) as predictors, and accuracy of solving the problem in minimum number of moves as the dependent variable , and participants and problems as random effects, are shown in Table Our results on the second measure of performance in SOC task, the time which the participant spent in preparing the moves prior to executing the first move, revealed that the problems which required greater number of moves to be solved  also consumed more preparation time (planning) prior to the execution of the first move Fig. . HoweverLinear mixed-effect regression to log-transformed initial thinking time, with participants and SOC problems/tasks set as random effects, language distance and minimum number of moves 2, 3, 4, 5) as fixed effects revealed the expected significant effect of the number of moves Table . However as fixedFinally, in order to further explore potentials of this task, we reduced the dataset to those problems that were particularly hard for our participants . The category of problems that could be solved in two moves was excluded as none of these were solved in a nonminimum number of moves. The descriptive data presented in Fig. To test the newly proposed variable, we selected the problems that were successfully solved in a nonminimum number of moves and performed a linear mixed-effect regression to initial thinking time. Participants and items were included as the random effects and language distance was included as the predictor. The results Table  indeed rFinally, Fig. Mean correct RT  is presented in Fig. This measure is calculated as the difference between RT to congruent trials and to incongruent trials  as fixed effects. As presented in Table Global switch cost is usually calculated as the difference in RT recorded in blocks with a single task (no task switching) and nonswitch trials in a mixed block. As with the congruence cost, we chose to test for the effect of the block type  on RT instead of calculating the difference and comparing it to zero. This time, the effect of language distance on global switch cost would correspond to the interaction between language distance and block type. Figure In order to test for the interaction between language distance and block type we conducted a linear mixed-effect regression to log-transformed RT, with participant as random effects. The fixed effects were language distance and block type . As presented in Table Local switch cost is typically calculated as the difference between the RT recorded in switch trials of the mixed block and RT recorded in nonswitch trials of the mixed block. As with the previous two measures, we tested for the effect of the switch instead of calculating the difference in RT. The effect of language distance on local switch cost corresponds to the interaction between language distance and switch. Figure We tested the interaction between language distance and switch by conducting a linear mixed-effect regression to log-transformed RT, with participants as random effects, as previously. The fixed effects were language distance and switch . Table This effect manifests as the asymmetry in the congruency effect for trials preceded by congruent and incongruent trials. In other words, the finding of SCE indicates that the congruency effect  is larger following congruent trials than following incongruent trials. Figure To test for the diagnostic triple interaction, a linear mixed-effect regression model was fitted to log-transformed RT, with participants as the random effects Table . The fixTo summarize, we compared the performance of three groups of participants—Hungarian–Serbian bilinguals , Slovak–Serbian bilinguals , and Serbian monolinguals, in two groups of tasks—those that rely dominantly on memory , and those that rely dominantly on executive functions . The three groups of participants exhibited equal performance in DMS, PAL, and SWM (both on the storage-based measure and the measure of executive functions). However, in simpler SOC tasks  Hungarian–Serbian bilinguals spent less time preparing  as compared with Serbian monolinguals and Slovak–Serbian bilinguals. This difference was not observed in more difficult SOC tasks (which can be solved in a minimum of four or five moves). For these complex tasks, the difference between bilingual and monolingual participants  seemed to emerge only for those tasks which were solved using more than a minimally required number of moves (those where participants struggled before successfully reaching the solution). Finally, in the AST task, although there were no differences in congruence cost and global switching cost (mixing cost), Hungarian–Serbian bilinguals were faster overall and demonstrated reduced local switching cost as compared both to Serbian monolinguals, and Slovak–Serbian bilinguals.In short, we observed no differences among the three groups of participants in tasks that rely on memory. On the other hand, in tasks that rely on EF, we observed an advantage for high-distance bilinguals (Hungarian–Serbian) over both monolinguals and low-distance bilinguals (Slovak–Serbian). Importantly, although this advantage was moderate, it was observed in tasks that include multiple executive functions .The current study was the first to investigate memory and EF in monolingual and bilingual speakers of three underresearched languages/language combinations: Serbian, Slovak–Serbian and Hungarian–Serbian. Our aim was to test the widely held but increasingly debated claim that there exists a bilingual advantage in tasks assessing EF in young adult bilinguals who are argued to be at the peak of their cognitive functioning. More specifically, we wanted to establish whether there exists an effect of language combination on the EF skills in speakers of typologically different languages: Slovak and Serbian, two Slavic languages, are closely related, while Hungarian and Serbian are not.Taking into account concerns raised in recent reviews , our results revealed no difference in the performance of the Hungarian–Serbian bilinguals, Slovak–Serbian bilinguals, and monolingual speakers. While the literature on the memory differences in bilinguals and monolinguals is inconclusive .The Hungarian bilinguals’ better performance in our study seems to support the view that the skills required to maintain separation between typologically unrelated languages lead to more enhanced executive functions. An explanation we would like to entertain (we thank an anonymous reviewer for pointing us in this direction) relies on recent findings of Branzi et al. , which, The exertion of more cognitive control thus may lead to an advantage in executive function for bilingual speakers of languages that are typologically distant, such as our Hungarian–Serbian bilinguals, compared with our Slovak–Serbian bilinguals. Building on studies that report multilinguals to engage in the process of preparing for the production of their target language, even before knowing which words to say  tend to perform better than the bilinguals whose language combination includes closely related languages (Slovak and Serbian), suggesting that the skills required to maintain separation between unrelated languages may lead to even more enhanced EF skills.ESM 1(DOCX 44 kb)


Nystagmus tests to diagnose BPPV are still relevant in the clinical evaluation of BPPV. However, in everyday practice, there are cases of vertigo caused by head movements, which do not follow this sign in the Dix-Hallpike maneuver and the turn test.To characterize BPPV without nystagmus and treatment for it.A non-systematic review of diagnosis and treatment of benign paroxysmal positional vertigo (BPPV) without nystagmus in the PubMed, SciELO, Cochrane, BIREME, LILACS and MEDLINE databases in the years between 2001 and 2009.We found nine papers dealing with BPPV without nystagmus, whose diagnoses were based solely on clinical history and physical examination. The treatment of BPPV without nystagmus was made by Epley maneuvers, Sémont, modified releasing for posterior semicircular canal and Brandt-Daroff exercises.From 50% to 97.1% of the patients with BPPV without nystagmus had symptom remission, while patients with BPPV with nystagmus with symptom remission ranged from 76% to 100%. These differences may not be significant, which points to the need for more studies on BPPV without nystagmus. Benign Paroxysmal Positional Vertigo (BPPV) is one of the most frequent vestibular disorders. Its incidence varies between 11 and 64 cases per 100 thousand inhabitantsAccording to Weider et al.As far as etiopathogeny is concerned, SchuknechtThe most often involved semicircular canal in the BPPV is the posteriorAmong the causes associated with BPPV, the most common are head injury (17%) and vestibular neuritis (15%). Other causes include vertebrobasilar ischemia, labyrinthitis and surgical complications from middle ear intervention and after prolonged rest. Nonetheless, most of the cases seem to be idiopaticFor diagnostic purposes, the positioning nystagmus investigation enables the localization of the side and that of the damaged canal and the distinction between canalithiasis and cupulolithiasis, being important to guide the most indicated rehabilitation exercises for each case, a fundamental part of treatmentDix & Hallpike-The Dix and Hallpike maneuver have a positive predictive value of 83% and negative predictive value of 52% for the diagnosis of posterior and anterior semicircular canal BPPV, and a common mistake is not to perform it in patients complaining of vertigo or dizzinessNystagmus in these cases is considered important to characterize the BPPV until current days. Nonetheless, in the clinical practice, there are cases of vertigo caused by movements such as: laying down, turning from one side to the other in bed, fast head movements horizontally and bending over, without nystagmus in the Dix -Hallpike maneuaverBPPV studiesTreatment with exercises and vestibular rehabilitation repositioning maneuvers depend on the identification of the damaged canal and are not specific for each one of themGiven the aforementioned, added to the scarce publications on BPPV without nystagmus, also called subjective or atypical, this non-systematic review is fully justified and its goal is to characterize the BPPV without nystagmus, as well as the treatment approach in such situations.We searched for papers in the following databases: MEDLINE, BIREME, SCIELO, LILACS, PUBMED starting from the keywords which characterized the topic: BPPV, lack of nystagmus, diagnosis and treatment, in Portuguese, English and German.The selection criteria for studies were: published between 2001 and 2009; clinical studies with adults and literature reviews with emphasis in the diagnosis and treatment of BPPV without nystagmus. Added to this review is the summary, in English of a paper in ChineseOf the ten listed papers, we found nineAs we can see on BPPV without nystagmus is characterized by the clinical exam in which the patients complaining of brief BPPV spells without nystagmus and/or nausea associated with changes in head position did not have positional and/ positioning nystagmusCaovilla & GanançaWe found three probable explanations for the absence of dizziness and positioning nystagmus in head movement which would enable symptom and ocular phenomenon elimination at that time. The patients could have minimum calcium carbonate particles stuck to the cupule or floating in the affected semicircular canal, enough to cause nausea and/or vertigo, but not enough to cause nystagmus. In this situation, the affected labyrinth would be the one on the same side of the maneuver from which the patient reported dizziness seating down. Before treating the patient, the maneuver can be negative for BPPV in a first assessment and positive in another one, on the same day or in a different day. Many BPPV cases did not have positioning nystagmus or dizziness at the time of the maneuver, which does not rule out the diagnostic maneauverAnother explanation for the BPPV without nystagmus was proposed by Johkura, Momoo & KuroiwaGansThey did not present nystagmus in the diagnosis of BPPV in cross-sectional studiesDix-Hallpike was the one most used in the studiesThe treatment of BPPV without nystagmus was carried out by means of the EpleyAs far as treatment for BPPV patients without nystagmus is concernedIn the studiesIn the three prospective studies, there were no similarities in the follow up of these patients. One reassessed the patients after 5 daysMost of the BPPV cases, with or without nystagmus responded favorably to vestibular rehabilitation physical therapy procedures. Ganança et al.BPPV without nystagmus is characterized by vertigo and/or nausea in the absence of nystagmus, especially in the Dix-Hallpike and in the Sémont, Brandt-Daroff tests or in the turn test or lateralization maneuver. Frenzel goggles with infrared camera were not used in all the patients, but they may be useful.The treatment of BPPV without nystagmus may be carried out based on the typical history of BPPV and signs found upon physical examination, with vertigo. One should treat the side on which the signs were triggered by means of the Epley and Sémont maneuvers and the Brandt-Daroff exercises, or even, by means of the modified freeing maneuver for the posterior semicircular canal.Symptom remission among patients with BPPV without nystagmus who were treated was of 67.64%, with a subtle difference for patients with nystagmus (13% to 17%), which suggests the need for BPPV treatment, even in patients without nystagmus.


In 2013 the Brazilian National Health Surveillance Agency (ANVISA) determined that the leishmanin and tuberculin skin tests  were vaccine-like products, which redefined the manufacturing requirements for these reagents and resulted in what was then considered a temporary suspension of production of the LST ANVISA’s new biosafety regulations require TST to be produced in a biosafety level 3 (BSL-3) laboratory, but the production of LST requires only a BSL-2 structure. As a result, a lower investment over a shorter period was required to adapt the CPPI manufacturing plant . However, the CPPI, a public laboratory in the state of Paraná, does not have the necessary capital (approximately 30 million BRL) to produce GMP-grade leishmanin. A severe economic crisis in 2015, combined with the inherent lack of political interest in the category of neglected tropical diseases (NTDs), which includes leishmaniasis, finally halted production of the Montenegro test at the CPPI in 2016.The only social group that could demand that leishmaniasis be put back on the political agenda for science and technology are experts, clinicians, and other investigators who work with this disease daily. These leishmaniacs, as they call themselves, participate in various meetings and networks related to decision making and priority setting, including ChagasLeish, the Brazilian Society for Tropical Medicine, and redeLEISH. It is somewhat surprising that this group did not advocate the resumption of LST production, either in redeLEISH or in the 2016 closed meetings organized by the Brazilian Ministry of Health and PAHO, which I had the honor of attending. Instead, they highlighted the need for a new diagnostic test for cutaneous leishmaniasis.The technological and scientific advances in this field are so obvious that scholars call the current age the fourth industrial revolution or the age of digital healthcareOur society is described as future-oriented because it tends to reject anything that does not reach a certain level of technological performanceFuture-oriented discourses and blind faith in modernity distract us from the possibility of advocating a simple product, the Montenegro skin test. We can strive for better diagnostic tools, but in the meantime, the Brazilian government must protect LST. Brazil has the infrastructure and capacity in some public laboratories to produce GMP-grade quality leishmanin. Bio-Manguinhos, the Institute of Technology on Immunobiologicals, is one example; in fact, this public laboratory was the second-largest supplier of the Montenegro skin test to the Brazilian Ministry of Health until 2003, when the CPPI became the only supplier. Bio-Manguinhos stopped producing the LST because it is so inexpensive and simple to manufacture, preferring to focus on higher value-added products that augment revenue and streamline the manufacturing processThis may explain why LST is no longer manufactured worldwide, but this test can still make a difference in many social and/or remote settings because it is inexpensive and easy to use


The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research Cloud computing is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software. These features are the requirements and concerns raised by a larger audience on the internet. The world wide web has carved new paths and approaches to utilize the concept of the digital world at an optimum level. Cloud computing has emerged with this conception and has fulfilled many expectations. It has also brought new questions and challenges for this new ecosystem. Cloud computing serves corporate needs and caters to individual users, making its adaptation swifter than expected . The priSimilarly, the service model presented by cloud computing very justly provides core services to individual and corporate groups. Additional value-added services are as per the user’s choice but are not a compulsion. The core services , i.e., iMicrosoft Azure, Google Cloud Platform, Amazon EC2, Oracle, IBM, Cisco and many more cloud service providers (CSP) are bringing new services, pricing models and facilities to the consumer .n number of services, is the suitable candidate for application development design. Provisioning multiple services in different tiers or in one single tier generates the vital importance of sustainable performance [As discussed, cloud platforms are a service-focused computing environment with distributed shared resources and uses when you need a service model . Consideformance .The multi-tier model is a robust solution for providing an integrated service pack in a single machine to the end-user. Microsoft Azure and Amazon cloud services are based on the multi-tiered cloud environment. The benefit of the multi-tier cloud model is an optimal workload capacity and multi-node management, i.e., multiple users are able to be managed with different service requests in one multi-tiered model . To ensuTo ensure the maximum benefits and stable operations in a data centric cloud environment, it is essential to address the data storage, processing and navigation issues in the utmost detail to provide multi-dimensional solutions and frameworks. One core segment of this exercise is the reliability modeling for the cloud environment that leads to the development of the basis for cloud mining, predictive analytics, decision support systems, intelligent configuration systems and cloud ecosystem management. Assuming that all factors have a high ranking except the data reliability, then all other factors are compromised, and the purpose of the whole operation results in a data failure . All othMoreover, it also contains the recoveries from failure, backups and fault tolerance capabilities to ensure smooth operations. In cloud computing, these servers may have different physical locations and resources. They are connected through routers and internet by using virtualization, and the physical differences in locations are insignificant. Multiple servers can be treated as one giant server for the user in a virtual environment consisting of collective resources for computing, storage and the network. A hard disk drive (HDD) can be categorized into two types, i.e., HDD that can have volume storage, speed and connectivity. The second type of storage is a solid-state disk (SSD) which brings more speed, storage capacity and security. These types are having an impact on the software applications running the performance and response times. Currently a series of SSDs or a mix of SSDs and HDDs are used in data centers. The series are known as RAID for high performance and robust data processing, including concurrent virtualization and data influxes from different resources .As mentioned earlier, beyond free services, the public cloud offers a versatile and flexible pricing model engaging the concept of “pay as you use”, making it possible to build resources according to capacity and demand. This pricing model leads to a flexible cloud platform that is highly scalable as per the growing demand and technology requirements, along with a facility requiring minimal capital expenditure. Moreover, all service providers provide end-user data protection and recovery capabilities, which include redundancy to prevent losses in the event of any catastrophe. On the other hand, keeping a backup system is an expensive and technical exercise which is not suitable to every end user. A server with good computing and network capabilities always needs a fast, flexible and stable physical storage mechanism .There are different types of data failures in cloud computing such as software failure, hardware failure, power failure and network failure. All of these factors can lead to data failure and can affect cloud service failures. In a conventional file system, the failure may be a network outage, machine failure, drive failure and data corruption, as shown in Erasure coding is mainly used for protecting the data from failures in large-scale storage. It is also used to detect and correct errors in cloud computing. In erasure codes, a file can be divided into equal chunks. It also added the parity chunks that can be restored to recover the original file. Erasure codes can be divided into two categories: Maximum Distance Separable (MDS) and non-MDS .The replication technique focuses more on the cloud computing reliability process to maximize data availability and reliability. Low latency and minimal latency can be reached by consuming bandwidth overcapacity on the network. The lost data need to be restored in the alternative storage medium by retaining the emphasis on reliability. Furthermore, restoration is reactively and proactively divided into two groups. For replication, there are two techniques used. The replica will be generated with the reactive method after the failure. In a constructive method, the replica will be generated before failure occurs. In Static Replication, the total number and location of replicas are fixed. Random replication is used in HDFS, GFS, RAM Cloud and Windows Azure . In dynaThe storage segment of cloud computing is based on the data centers in terms of physical infrastructure. By a definition from Google, a data center is a cluster of buildings with various functions, numerous server machines and communication equipment to be linked together to develop a common environment with common maintenance and security needs. In terms of components, the physical structure can be divided into three main categories, i.e., server machines, storage and reliability. The server machines are designed for heavy processing, communication and storage facilities . The mulData storage in cloud environments is a big challenge for data reliability and trust issues. Both key attributes play a critical role in the survival and growth of cloud services and in attaining the trust level of cloud users. The tendency of engaging cloud storage services is expanding continuously, and the data management in cloud environments is becoming a big challenge in terms of data reliability, security and accessibility. This research aims to formalize a hybrid approach for data reliability in cloud storage management and also introduces a conceptual framework to improve reliability storage efficiency and latency in accessing data over cloud computing.This research has formulated the following research motivations;Reliability of data requires optimizing durability and data availability.Durability mitigates permanent failures and mitigates temporary failures by availability.In cloud data centers, different methods are used to increase the fault tolerance of the storage system.Replication is a proven process in cloud computing and data centers to maintain data reliability and efficiency. Mirroring and erasure coding both are valid methods and have their respective advantages and disadvantages. This research provides a hybrid framework to engage in mirroring and erasure coding for replication based on the advantages and performance of each method. Therefore, the proposed framework provides the optimal data replication and respective features to deal with the heavy volumes and associated risks.The following objectives are defined for this research;Designing cloud storage reliability assurance to evaluate storage properties.To produce an autonomous storage management model for improving storage efficiency.Formulating a model for data reliability in cloud storage management.Cloud storage systems are composed of large numbers of hardware and software components. Failures are the norm rather than the exception in cloud storage systems. Any failures such as hardware failures, power outages, software glitches, maintenance shutdowns or network failures in the cloud storage system will raise temporary data unavailability events and sometimes lead to permanent data loss. In spite of these failures, to provide reliable service to the customers, various fault-tolerant mechanisms are employed. To meet the large-scale storage needs of clients, cloud defines virtual storage using Network Attached Storage (NAS) and Storage Area Network (SAN) .The networked storage NAS and SAN are easily scalable in terms of both performance and capacity, and hence are highly influential in cloud storage systems. They use a distributed file system to organize data for storage and provide controlled data access to clients. Distributed File System (DFS) spreads data in a storage cluster which is composed of thousands of nodes. DFS applies data redundancy to improve the fault tolerance of cloud storage systems, and it spreads redundant data into nodes from different failure zones . DFS is Any failures in cloud storage systems mentioned above may lead to unavailability events from time to time. Whenever an unavailability event occurs, it activates data recovery to maintain durability and data availability. The data redundancy mechanisms employed in cloud storage systems are replication and erasure coding. Replication maintains multiple copies of data on distinct nodes from different failure domains. Replication is a straightforward fault-tolerant method. However, it is not an efficient solution for big data due to the volume of data. Erasure coding is a storage-efficient alternative reliability method .Strauss has mentioned the importance of reliability based on the availability of data, which is becoming a critical factor when considering or measuring the performance of any cloud service; therefore, it is becoming essential to consider the reliability methodologies while developing the cloud’s structure . Zhang hThe reliability and recovery of the data has not been evaluated at the infrastructure level only, and various scholars have developed and tested different algorithms to address various critical challenges. In the same year, Cheng proposed another framework for the reduction in failure and dependencies by improving the system’s reliability. The basis of this framework is also rooted in the infrastructural services in cloud computing . This frThe research related to reliability and availability has extended into the prevention of failures, identification of failures, performance prediction and defensive methodologies. Sharma has highlighted the importance of such indirect factors on the overall performance and reliability of the data. The preventive measures and predictive maintenance of the computing system have a long-term impact on the strategies for cloud computing storage, replication and recovery methodologies .The topic of cloud computing storage has become a more comprehensive area for researchers as it incorporates hardware or infrastructure as a service (IaaS), virtualization and computation capabilities, and more importantly, the configuration of virtual machines. Nachiappan has discussed the virtual machines scenarios of multiple configurations and the relevant impact on the performance of the cloud services. The importance of this work is related to the challenges of big data in cloud computing, and specifically related to the storage reliability and availability in case of virtual machine failures or weak configurations. They also discussed the role of security, preventive measures and storage methodologies such as static replication, dynamic replication, mirroring and erasure coding, to be specific .The Popular Hadoop Distributed File System (HDFS) uses three replicas. Hence, it can tolerate any two simultaneous failures with a storage overhead of 3x. The most popular Reed–Solomon can manage any four simultaneous failures with 1.4x storage overhead. Even though the storage efficiency of erasure coding sounds appealing, data recovery/repair in erasure coding involves enormous resource consumption. For example, data recovery in Reed–Solomon code increases disk I/O and network bandwidth by 10x compared to replication  . Data reWhen there is a failure in cloud storage systems, the objects that are resided in the failed zone will enter into a degraded mode. A delay is applied to recover any degraded objects to avoid any unnecessary repair. Degraded objects will remain in degraded mode from the time of failure utill complete recovery. Any data read request to a degraded object in replication is handled by redirecting requests to the next available replica. On the other hand, in erasure coding, a degraded object is reconstructed on the fly. In replication, the object is recovered by copying it from the next available replica, whereas in erasure coding, the object is recovered using the data reconstruction of any other k available chunks .The purpose of this framework is to develop provisions for the cloud users who are dealing with a heavy volume of data/big data in a distributed environment, as managing multiple virtual machines can engage both successful techniques instead of selecting one specific one. The framework is designed in three main layers to cater to the objectives described in the previous sections. It is also important to note that different erasure coding and data replication algorithms are already available with proven results and performance; therefore, the scope of this research work is to build a working environment in which various algorithms can work as per the requirement. Moreover, new and emerging algorithms are also manageable .In cloud computing, the main challenge is managing data prolifically and securely. As the cloud computing platform is designed for a distributed and shareable environment, at all service levels, i.e., IaaS, PaaS and SaaS, the concepts of distribution, load management and latency control are the main pillars. At the infrastructure level, the storage of data is completed using various methods, and the most prominent and effective methods are data replication and erasure coding. Both the methods are focused on data reliability, availability and profiling to ensure the data’s integrity and security. Both methods have pros and cons related to the swift recovery of data, latency and the mechanism through which both methods store the data. This research proposes a hybrid framework that uses both methods, i.e., replication and erasure coding, to provide optimum data integrity .The expected outcome is fewer data losses, failures and swift recoveries using optimum methodologies. Erasure coding uses RAID 5/6, while replication uses mirroring on RAID-1. Replication using mirroring may use more space but provides better results in terms of the reliability of data due to the redundant mechanism, while erasure coding is more economical on storage and provides swift recovery in case of failure. The fault tolerance and recovery capacity of erasure coding is better than replication, but on the other hand, replication provides more reliability and integrity though at a slower pace, with an expense in terms of storage by using more space.Most manufacturers now include a system restoration disc computer to help reduce the difficulties and frustrations of fixing a system malfunction. This instance is manageable by configuring Kubernetes in a cloud configuration, and the proposed framework uses CNI (cloud network interface) and CSI (cloud storage interface) to identify the healthy nodes and perform an assessment based on the virtual machine configuration, i.e., the compute, storage and network properties available with the configuration manager. In other words, the default configuration of all virtual machines resides with the configuration manager, while the dynamic status of the virtual machines is captured through CSI and CNI interfaces that transform into the health status of the virtual machines, i.e., in the case that, due to a recovery instance, erasure coding is activated on virtual machine number 3, the dynamic value of its storage will increase. The CSI and CNI interfaces pass the value to the VM health controller, and in the case of any room being made available for dynamic replication or erasure coding by creating an instance on another virtual machine, it will start the load balancing accordingly to create space for the current instance.In the case that all VMs are busy and not able to accommodate the current instance, then instead of crashing the system, the current instance will be in the cache cue. Therefore, as soon as the virtual machine is available in processing terms, i.e., suitable compute, storage and network properties, then it will start the recovery or replication procedure as planned. The physical resource layer consists of hardware variants in terms of storage devices, i.e., racks, nodes, disks, etc. As we are considering the cloud platform, therefore, this layer physically resides with the cloud service provider in the case of a public, community or hybrid cloud. In the case of a private cloud or a corporate data center, this layer shall be categorized as in-prim hardware services. It was mentioned before that erasure coding works on RAID 5/6 and replication uses RAID-1, and the physical resource layer manages both. The accessibility to this layer is provided through virtualization in terms of different virtual machines. The cloud platform is flexible enough to provide a customized configuration of each virtual machine, though the overall capacity of all virtual machines is dependent on the physical resource available. To select between two storage services, i.e., erasure coding and replication, this layer is formulated to provide data cluster health and device failure possibilities using a fabric agent for host management. It is notable here that device failure predictions are based on the possible failure of the erasure coding or replication method. To elaborate further, if replication uses HDFS for mirroring and faces a hardware, bandwidth or logical fault, the fabric agent will provide a health alert for the current data cluster and evaluate the possibility of failure to predict the event. The same process is applicable for erasure coding, e.g., in the case that erasure coding is using hyper-convergent storage for video streaming and facing high latency, hardware failure or a split modulation problem, the fabric agent will perform the same function as mentioned before. The objective of this module is to take necessary action, i.e., switching to the other method within a minimal timeframe without disturbing the data operations and management, as shown in This layer is linked with the prediction layer and operates in accordance with the fabric agent’ instructions. This framework takes erasure coding and replication management modules as hosts, while the fabric agent is the host controller to assign tasks and operations received from the virtual machines or cloud users. As depicted in the diagram, the hybrid layer is split into main modules, i.e., erasure coding management and replication management, with two connecting functions: a cache configuration system and selection for the prediction layer. This layer also contains the respective algorithms for erasure coding as well as for replication. New algorithms can be added into the same modules, and therefore, the optimum solution for storage management is possible with the most recent and best algorithms.The management sub-module contains the algorithms with the encoder and decoder sub-modules. This framework works on this assumption that the available algorithms are proven and tested; therefore, the encoder and decoder are able to perform their respective functions. Virtual erasure coding, HDFS erasure, hyper-convergent erasure and Reed Solomon erasure are a few examples. From the prediction layer, the fabric agent will provide the task to the host, i.e., erasure coding management with the data cluster health status, and by default, both methods will be used based on the capacity and availability of physical resources. In the case of replication management failure, the erasure coding management module will resume the task and start recovering data from the replication module. For this purpose, the replication module has one dedicated node in the erasure coding module. In the case of a bad health flag on the data cluster or the predictive failure of a device linked with the replication management module, the erasure coding module will start recovering and assuming data tasks through the dedicated node.Replication management is connected with the prediction layer through the selection function for two main tasks. The first is to take the backup of a data cluster with a bad health status declared by the fabric agent. This data cluster is used by erasure coding or replication management module, and this module shall complete the mirroring of that cluster with both methods. It is also possible that certain parts or segments of the cluster need to be backed up. Using various algorithms, e.g., genetic algorithms, multi-tier data algorithms, mirroring algorithms, etc., will ensure a timely backup. The second task is related to the failure of erasure coding. The replication module will take the backup of the encoded data from the erasure coding management. A dedicated node shall also be available in the replication management module to ensure data cluster health status replication, predictive device failure, and possible erasure coding failure or high latency. This switching process is seamless and quick to provide an uninterrupted data service without failure. While in the case of the failure of any of the two components, a swift and timely recovery is possible through this hybrid approach.The system monitoring layer maintains the stable working of the whole structure by evaluating the changing configurations, node adjustments and virtual machines’ status as per the configuration manager. In case of a particular node failure, the load is distributed by engaging close with virtual machines to provide an instance for the recovery process to streamline the system again.We have used the Streamlit  data streaming and erasure coding. Streamlit is a GNU-based algorithm that is primarily designed to develop erasure coding for high-volume distributed data management. It uses machine learning to engage statistical methods in neural networks to select a suitable encoding algorithm for the data cluster. The validation of the proposed framework has been completed by using a video streaming scenario, as it is heavy in volume and also requires a swift and smooth recovery without interruption.As per the scope of this research, a hybrid approach is designed to formulate a balance between replication and erasure coding to gain optimum storage reliability and recovery performance. In the validation segment, purpose failures were induced to evaluate the performance and activation of erasure coding to provide a swift recovery and continuation of service. Replication and erasure coding are tested with heavy-volume data processing as in normal scenarios. The cloud storage methodology with replication works fine and provide successful results. The aim of this research, as explained in the previous section, is to provide such a hybrid method that can generate robust performance in case of high data volumes and have the capacity for failure tolerance and the continuation of operations without compromising the storage reliability and recovery characteristics.The scenario is developed using the existing technology and best practices under consideration. Current cloud storage methodologies extensively use RAID technology for the physical storage and manipulation of the data. Different RAID levels have proven to have performance and reliability pros and cons. To evaluate the proposed framework, this research has engaged RAID-0 to RAID-6 for data diversification and spreading while the most commonly used RAID-5 and RAID-6 are used for the replication and erasure coding. RAID-5 and RAID-6 both are configured for the mirroring purpose, while the erasure coding is deployed on RAID-6 only for focused and more interpretable results. Further grouping between RAID-1 and RAID-5/6 is categorized on the basis of failure tolerance. RAID-1 is grouped with RAID-5/6 with a fault tolerance of 1, with 200 gb and 133 gb of capacity, respectively. The other group contains RAID-1 and RAID-5/6 with a fault tolerance of 2, with a capacity of 300 gb and 150 gb, respectively. Dataset, RAID configuration, RAID grouping, and mirroring and erasure coding with various capacities have been performed. For operations, the data are used with various protocols that provide the more complex scenarios at the administrative level to ensure the robustness and impact of the proposed framework for reliability and recovery using replication and erasure coding.The dataset we have used is the KDD12 dataset for the purpose of validation, consisting of approximately 1,074,992 records. A random selection of 322,98 records has been used for the mirroring and erasure coding experiment on various RAIDs, as depicted in the following table. The RAIDs for the replication experiment are configured as shown in For replication, the following blocks are configured on RAID-5 and RAID-6 as shown in For mirroring and erasure coding, the following configuration is applied on the RAID-6, as shown in The final configuration for the scenario is given in As is visible, RAID-5 and RAID-6 reciprocate RAID-1 mirroring, while RAID-3 and RAID-4 use mirroring, but in a block and striping format. If RAID-1 (mirroring without striping) will impact erasure coding, then upgrading the other RAID formats becomes easy and more logical. Therefore, from a data spread point of view, all RAIDS are engaged, while the configuration above is being used from a reliability and recovery perspective. The failure limit is also defined for each RAID classification, and with the exception of RAID-0 and RAID-6, all other RAIDS have a failure threshold on one drive, while RAID-6 is configured as a failure threshold on two drives. There is a parity diversification for each RAID as well, ranging from no parity, shared parity, dedicated parity, distributed parity and double distributed parity.The parity range also significantly impacts the replication and performance of the RAID; therefore, it is highly important to evaluate various configurations to understand the validation of the proposed framework. Double distributed parity is assigned to RAID-6, while RAID-5 uses distributed parity, but both are complex parity cases. Therefore, with the other configurations mentioned in the previous section, the scenarios developing at RAID-5 and RAID-6 are challenging and depict the real-life complexity level and robustness requirements at an optimum level.In cloud computing, there are other storage methods, but as mentioned earlier, in the literature, we have identified the best practices for storage, and RAID is one of the most usable methodologies in various cloud configurations.Initially, data parameters are used for training purposes to engage intelligence, which is required to develop autonomy as the system obtains maturity. The switching between replicated mirroring and erasure coding is essentially based on the intelligent flagging and understating of the anomaly patterns by the machine itself.Therefore, the parameters are trained to learn the anomaly flags, and these learning datasets are generated through a number of iterations. To make the learning more independent, the column names are also dynamic, and the machine learns the columns and transforms a training structure every time instead of fixing on specific columns, meaning that the same learning is applicable on multiple cloud and RAID configurations without any modifications, as it is going to pick the columns dynamically to formulate the learning parameters.As per the scenario explained in the previous section, the initial rationale is linked with the data’s framing and evaluation. Data are linked with various protocols assuming that various communication mechanisms are attached to the cloud, and therefore, the nature and source of data needs to be rationalized before proceeding towards the learning and testing of the system for mirroring and recovery. The following are the data framing results over multiple iterative engagements to evaluate various possibilities, as shown in The training dataset has generated the categories and columns shown in Data are linked with various protocols assuming that various communication mechanisms are attached with he cloud, and therefore, the nature and source of the data needs to be rationalized before proceeding towards the learning and testing of the system for mirroring and recovery. The following are the data framing results over multiple iterative engagements to evaluate various possibilities. The results show the normalization of the data to be processed for the pre-training adjustments. The results show that the tcp and udp source using ftp and http protocols though the private channel does not reach the normal value.Further data processing and learning shows the private data channel generates rejection of tcp instead of proceeding towards restoring. The other examples show the ftp data again reaching a normal value but not proceeding towards mirroring or restoring. Similarly, another case has generated an association pattern for mirroring and showing the status of the restoration of tcp.The training dataset has generated the categories and columns shown in the aforementioned table. The dataset is segregated into the host mirror’s server rate and the transition server rate, while the recovery is also split into these types, i.e., host recovery rate and host server recovery rate. The purpose is to manage the server-side storage as well as the VM-level storage. Another aspect covered in this activity is mirroring, which not only required for data at rest for servers but for data in transient. The results provide the values for the proceedings. The following graph summarizes the results. The host mirror’s server rate for the mirror-5 configuration shows an overall promising result, but on the other hand, the recovery using the erasure-6 configuration is also prominent.The further processing of the data focuses on mirroring and erasure coding with reference to the mirror, transit and restoration. These parameters are evaluated with the same modes, i.e., host mirroring, transit mirroring, host recovery and host recovery at the server end. The results are showing lower rejection in the case of mirroring, while rejection is showing higher rates in the b recovery configuration at the server and virtual machine level. Similarly, erasure coding and mirroring show significant trends in host mirroring at the server end, while the restoration is higher on the recovery modes. The erasure and mirroring show significant results at the server end, while these results are not as good when considering data in transit.Now, for the scenario built on different configurations of the RAID, the results show the analysis of RAID-1 to RAID-6 with respect to erasure coding, erasure coding with parity and mirroring combinations with all parameters. RAID 1 to 4 are used in the default configurations, while RAID-5 and RAID-6 are used in different combinations/configurations, i.e., RAID-5a and RAID-5b, while RAID-6 is configured in three different modes labelled as a, b and c. The mirroring instance engages all the configurations. As is visible in the graph, mirroring RAID-6c and RAID-6a are on the top for mirror and replication. Both RAID modes are hybrid in nature as configured previously. The instance occurring as ER1—erasure coding instance—engages RAID-6c, RAID-6b and RAID-6a as the most significant configurations, while RAID-4 also has a prominent value that shows that the best performance of erasure coding is in hybrid mode along with the mirroring instance, but in the case of only erasure coding, the RAID-4 value shows the impactful behavior of erasure coding for recovery and reliability. The second instance, or erasure coding ER2, takes RAID-6, RAID-6b and RAID-5b as the parameters, working in a hybrid mode only. This proves that the proposed framework provides significant results regarding reliability and swift recovery performance in cloud storage.The results show the best performance and coherence in a hybrid manner, i.e., mirroring and then handing over to erasure coding for recovery. The Erasure-6 and Mirror-5 configurations are hybrid modes. Data at rest and data in transit are both addressed successfully.Further processing of the data focuses on mirroring and erasure coding with reference to the mirror, transit and restoration, as shown in The results in The second instance of erasure coding ER2 takes RAID-6, RAID-6b and RAID-5b as the parameters, which only work in a hybrid mode, as shown in Now, for the scenario built on different configurations of the RAID, results are shown in The focused results for the erasure coding as shown in In cloud computing, various service providers extend different productivity tools to enhance the user experience. The proposed framework evaluates the need for dynamic replication using mirroring or the recovery process using an erasure coding module. The replication and relevant tasks can be configured to have a better visual response by managing CNI and CSI, as mentioned before. The ongoing activity is viewable on virtual machines.Both processes will consume computation and storage utilization; therefore, in virtualization monitoring, the visible processes of Reco (Recovery) are visible along with Opm (Optimization) and local. Virtual machines engaged in replication shall switch to local, and after replication, shall have the status of optimized, and in the case of recovery activity using erasure coding, the status will change to recovery.The structure of the dataset after the training and testing shows a minimal error rate, with erasure coding having value ranges between zero and one. The focused results for the erasure coding are extracted with RAID configurations, as is visible in Cloud computing provides different solutions for data safety, backups and replication. All these methods are independent and work separately, although these methods and tools have common parameters and can work together. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding was proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery.The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence and provide good results for the current framework, while highlighting the interesting and challenging paths for future research.


Feature selection is often used to identify the important features in a dataset but can produce unstable results when applied to high-dimensional data. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the results of multiple base feature selectors. However, a threshold must be applied to the final aggregated feature set to separate the relevant features from the redundant ones. A fixed threshold, which is typically used, offers no guarantee that the final set of selected features contains only relevant features. This work examines a selection of data-driven thresholds to automatically identify the relevant features in an ensemble feature selector and evaluates their predictive accuracy and stability. Ensemble feature selection with data-driven thresholding is applied to two real-world studies of Alzheimer's disease. Alzheimer's disease is a progressive neurodegenerative disease with no known cure, that begins at least 2–3 decades before overt symptoms appear, presenting an opportunity for researchers to identify early biomarkers that might identify patients at risk of developing Alzheimer's disease.The ensemble feature selectors, combined with data-driven thresholds, produced more stable results, on the whole, than the equivalent individual feature selectors, showing an improvement in stability of up to 34%. The most successful data-driven thresholds were the robust rank aggregation threshold and the threshold algorithm threshold from the field of information retrieval. The features identified by applying these methods to datasets from Alzheimer's disease studies reflect current findings in the AD literature.Data-driven thresholds applied to ensemble feature selectors provide more stable, and therefore more reproducible, selections of features than individual feature selectors, without loss of performance. The use of a data-driven threshold eliminates the need to choose a fixed threshold a-priori and can select a more meaningful set of features. A reliable and compact set of features can produce more interpretable models by identifying the factors that are important in understanding a disease. Healthcare datasets present many challenges to both machine learning and statistics alike. Their data are often heterogeneous, censored, high-dimensional and have missing information. In high-dimensional datasets, with a large number of features or variables and a small number of samples, typically only a small proportion of features may be relevant to the condition under investigation. Feature selection is generally used to reduce the dimension of the data, improve understanding of the problem and produce more interpretable models by identifying the factors that are important in understanding a disease . A reliaHowever, applying feature selection to high-dimensional datasets often produces unstable results . StabiliFeature selection ensembles aggregate the results of multiple base feature selectors to improve stability and predictive accuracy . One draTypically, simple univariate filters have been used as the base feature selectors in feature selection ensembles because they are computationally inexpensive, but they do not account for the interactions between features. More recently feature selection ensembles have been created from multivariate models , 9 and tThe focus of this work is to develop and test several data-driven thresholding methods in a novel context, that of ensemble feature selection using multivariate base feature selectors. The first method uses the 75% quartile of the feature importance scores as a threshold. The second method uses kernel density estimation (KDE) to cluster the feature importance scores and exclude the irrelevant features based on these clusters. The final method uses permutations of random probes , which aTo demonstrate the applicability of these methods to clinical data, they are applied to data from two quite different real-world Alzheimer's disease (AD) studies—the Sydney Memory and Ageing Study (MAS) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). AD is a progressive neurodegenerative disease that results in declining cognitive function, such as memory, reasoning ability and executive function, and is ultimately fatal. Although the cause of this disease is not completely understood, the underlying pathological processes begin at least two to three decades before overt symptoms appear . This prEnsemble feature selection has rarely been applied outside the context of classification. Data in healthcare, however, are often censored, meaning that the event of interest has not occurred during the study period, so the final outcome is unknown. Censored data are particularly common in AD studies as it is a slowly developing disease. The presence of censored data precludes the use of standard classification and regression techniques, but several machine learning algorithms have been adapted for survival analysis to handle censored data. It is these algorithms that we have chosen to investigate in this study, with the aim of expanding the use of ensemble feature selection to survival analysis. The methods developed here are applied to three real-world AD datasets to identify biomarkers for AD.This work overcomes the limitations of previous studies on ensemble feature selection in several ways. First, previous studies have typically applied a fixed threshold to determine the final set of selected features. A fixed threshold does not adapt to different sized datasets and may include some irrelevant features or omit some important features. Our work overcomes this problem by developing data-driven thresholds that can automatically adapt to the size of the dataset under investigation and therefore eliminate the need to test different fixed thresholds. Second, ensemble feature selectors have often been constructed from simple univariate filters, which do not account for the interactions between features that are common in complex biological systems. Although more recent works have begun to use multivariate embedded feature selectors to overcome this problem, our work explores a number of feature selectors that have not been used in an ensemble of this type before, as well as adaptations of feature selectors for censored data. Finally, all previous studies referenced in this work have been applied to the task of classification only. Our work ensures that other tasks, including regression and survival analysis, can benefit from the superior performance and stability achieved by ensemble feature selection.Ensemble feature selectors apply a base feature selector to multiple subsamples of the training data, aggregate the results and apply a threshold to the resulting feature set. In the same way that bagging, boosting and stacking can improve the performance and reduce the variance of supervised learning methods, ensemble feature selection aims to improve the stability and predictive accuracy of the final subset of selected features . The choEnsemble feature selection was first proposed by Saeys et al. , who conOther researchers have since conducted similar experiments, varying the feature selectors, the aggregators and the number of feature subsets in the ensemble , 13–19. Most existing studies of feature selection ensembles use only simple filters as base methods, as these are the most computationally efficient methods to apply to high-dimensional data. The filters chosen most frequently include chi-squared, information gain and ReliefF , 13, 18.2(n) where n is the total number of features ,features  of the tAs the number of relevant features is not known a-priori, a fixed threshold could include some irrelevant features or alternatively reject some relevant ones. Data-driven thresholding can potentially overcome this problem and also free the user from having to select and test different fixed thresholds for each model .Very little work has been reported on developing an automatic or data-driven thresholding method for ensemble feature selectors and this is an open area of research. Some early related works used the "biggest gap" between consecutive aggregated values as a point of threshold . While iSeijo-Pardo et al.  experimeOther researchers have investigated how to separate the important features from the redundant ones in individual feature selectors that return an importance score for each feature, such as random forests , 21–23. One-dimensional clustering of the feature importance scores is another method that may be useful in determining a threshold. The popular K-Means clustering method  has seveRodriguez and Liao  proposedm rankings may contain ties, be incomplete, and may be weighted.The problem of combining several ranked lists into a single final ranked list has been studied in fields as diverse as information retrieval, voting theory and bioinformatics, and various techniques have been proposed . In statSimple mathematical combinations, such as the mean, median or sum of the feature ranks or weights, are effective aggregation techniques and widely used , 14, 16.Wald et al.  carried In some cases aggregation and thresholding are combined. Kolde et al.  proposedRRA can be expressed as follows. If r is an ordered vector of ranks and k features, where k must be chosen in advance [k items seen so far is maintained and when all items in that set are greater than or equal to the threshold, the algorithm terminates. Therefore, it performs both aggregation and thresholding.Then the final score for the rank vector r is defined as the minimum of p-values: advance . Elementk rankings are required.Another algorithm from the domain of information retrieval that can perform both aggregation and thresholding is the MedRank algorithm . The MedThis work proposes three different data-driven thresholding techniques for ensemble feature selection, adapted from other areas of research and applied in a novel context. These techniques are tested with two different real-world AD datasets and compared to a selection of fixed thresholds.Somol and Novovičová  studied stability index has been used by several authors investigating the stability of feature selection subsets [1, S2 is defined as:Kuncheva's  subsets , 13, 16.adjusted stability measure [Lustgarten's  measure  is an imrelative weighted consistency of a set of feature subsets is an ideal measure as it meets the desired properties and does not overemphasise low-frequency features. The relative weighted consistency f is the number of occurrences (frequency) of feature Somol and Novovičová's  relativeExperiments in this work were conducted on data from two real-world AD datasets—the Sydney Memory and Ageing Study  and the The two datasets are quite different in terms of their study cohorts, data collected and depth of investigation and as such their features are not comparable. Instead, the aim in applying the methods developed here to these two datasets is to demonstrate their applicability to clinical data.The Sydney Memory and Ageing Study (MAS) is a population-based cohort study aimed at examining the characteristics and prevalence of mild cognitive impairment and dementia. Full details of the study can be found . The MASThe experiments reported here used only the baseline data, collected in the first wave of MAS. Participants from a non-English-speaking background were excluded, leaving 873 participants from the original 1037. The event of interest in the survival analysis was a diagnosis of possible or probable Alzheimer's disease, over a period of 6 years, from wave 1 to wave 4 of the study. During this period 64 people developed Alzheimer’s disease, indicating a censoring rate of 93%.The Human Research Ethics Committees of the University of New South Wales and the South Eastern Sydney and Illawarra Area Health Service granted ethics approval for the MAS study and informed written consent was given by all participants and informants. The MAS study and this work were carried out in accordance with the MAS Governance guidelines, which are based on relevant University of New South Wales and National Health and Medical Research Council research and ethics policies.The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD).ADNI participants were aged 55–90 years at enrolment and were recruited from 57 sites in the United States and Canada. The ADNI data set contains data from a clinical evaluation, neuropsychological tests, genetic testing, lumbar puncture, and MRI and PET scans. Subjects who participated in ADNI phase 1 were selected for this study. The event of interest in the survival analysis was a diagnosis of probable AD, over the period of the ADNI 1 study. A total of 200 participants with early AD were enrolled at the start of the study and a further 237 participants developed AD during the course of the study. Data that were used in forming a diagnosis of AD, have not been used in the models developed here to predict AD.mice [To prepare the data for the feature selection algorithms, missing data were imputed using the method of multiple imputation by chained equations in the R package mice . ImputatContinuous features were normalised, by subtracting the mean and dividing by the standard deviation, and multiple values for the same measurement, e.g. blood pressure, were averaged. Levels of categorical features containing only a small number of samples were combined where possible. Further details of pre-processing steps can be found .mlr (Machine Learning in R) [The R  package ng in R)  was usedng in R) .The ensemble feature selectors were constructed from six different base feature selectors, each capable of selecting features from high-dimensional, heterogeneous, censored data. Four sparse methods, which return a subset of important features, and two filter methods, returning a score for each feature, were chosen. The four sparse methods were penalised regression for the Cox model  were applied to the results of the filter methods, but as the sparse methods already select a subset of features, no further thresholding was applied to their results. A Ridge survival analysis model was trained and tested on the reduced dataset and the performance and stability of the individual models were compared to those of the ensemble models. The Ridge was chosen because of its superior performance in previous experiments .Homogeneous feature selection ensembles were constructed by applying the same feature selector to 50 bootstrapped samples of the training data, producing 50 subsets of features, as shown in Fig. Mean rank (MR): The features in each subset were ranked according to their feature importance scores or weights and the mean of these ranks across all subsets was taken as the final aggregated score. The features were again ranked by this aggregated score.Mean weight (MW): The weights or feature importance scores were averaged across all feature subsets and the features were ranked by the aggregated score.Robust Rank Aggregation (RRA) [on (RRA) : This meThreshold Algorithm (TA): In each fold of the training data, the number of items to be returned, k, was set as the mean length of the 50 feature subsets generated by the ensemble. Elements were accessed sequentially from the ranked lists. At each sequential access a threshold equal to the sum of the scores in that access was set. A list of the highest scoring k items seen so far was maintained. When all items in that set were greater than or equal to the threshold, the algorithm terminated. This algorithm performs both aggregation and thresholding, so no other thresholding techniques were appliedMedrank Algorithm (MA): In each fold of the training data, the number of features to be returned, k, was set as the mean length of the 50 feature subsets generated by the ensemble. Features were accessed sequentially from the ranked lists. When a feature appeared in more than 20% of the ranked lists, it was output to the aggregated list. . If the length of the aggregated list reached k, the algorithm terminated early.Five different aggregators were used to combine the feature subsets into a final feature set. Three of these aggregators also perform thresholding:A mixture of fixed and data driven thresholds were tested for comparison. Three different fixed thresholds were chosen—10%, 25% and 33% of the total number of features, applied after aggregation.Three different data-driven thresholds were also tested. The first method used the 75% quartile of the feature importance scores as a threshold. In preliminary testing the 25% and 50% quartiles were also examined, but the 75% quartile provided consistently better results, and so only that method has been included here.The second method used kernel density estimation (KDE) to cluster the 1-dimensional importance scores, and this is the first time KDE has been used as a threshold in the context of ensemble feature selection. The cluster centres are defined as the local maxima in the density of data points. Each point is assigned to the same cluster as its nearest neighbour of higher density. In the case of 1-dimensional data, KDE can be plotted, as shown in Fig. KDE determines the number of clusters from the data but still requires a bandwidth  to be selected. Here the well-known rule of thumb, Silverman's rule, was used to select the bandwidth . If the The final method of data-driven thresholding tested used random probes to determine the boundary between the relevant and irrelevant features , 21–23. Random probes were originally developed for use with continuous numeric data and feature selectors that return a score for each feature, so some adjustment was necessary to use them with heterogeneous data and sparse feature selectors. First, it is possible that a sparse feature selector may not select any of the random probes, meaning that none are given a feature importance score, and therefore a comparison cannot be made between the importance of the probes and the importance of the features. In this case all of the features selected by the sparse feature selector were considered relevant. Second, randomly permuting Boolean features  and categorical features  can leave some values in the same position, and so some of the random probes can achieve quite high importance scores, potentially eliminating truly important features. The random probes generated from Boolean features were excluded from the dataset for this reason. However, the random probes generated from categorical features were retained as the larger number of possible values ensured a more random permutation.The prediction accuracy of the feature selection ensembles was assessed by the value of the Concordance Index (C-Index) achieved by a RIDGE survival model trained on the features selected by the ensemble. The Ridge model was chosen for its superior performance and stability in prior experiments . The C-Irelative weighted consistency [Each ensemble feature selector or individual method generated 25 final feature subsets from the 5 repeats of 5-fold cross validation. The stability of each ensemble was measured by applying Somol and Novovičová's sistency  to theseThe aim of this work is to develop and test a variety of data-driven thresholds for use with homogeneous feature selection ensembles, so as to free the user from having to select a fixed threshold. Three methods of data-driven thresholding were applied in a novel context and evaluated—the 75% quartile of the feature importance scores, KDE and the best random probe score. Three existing methods of thresholding, that combine thresholding with aggregation, were also tested—the RRA, and the Medrank (MA) and Threshold (TA) algorithms from the field of information retrieval. Three fixed thresholds were included for comparison. The thresholding methods were applied to homogeneous feature selection ensembles constructed from six different base feature selectors, capable of handling high-dimensional, right-censored data, and the stability and predictive accuracy of the ensembles were compared to their equivalent individual form. The six base feature selectors were penalised regression for the Cox model . However, in the case of the filter methods, which return a score for each feature, a threshold must always be applied, even in the individual form, to select the most useful features. Therefore, the filter methods have multiple results for the individual form of the model, one for each fixed threshold and for KDE.Song et al.  comparedThe graphs in Figs. The best threshold overall was determined by taking the average Euclidean distance from the origin for all models using each threshold. These results can be seen in the graph in Fig. The fixed thresholds show varying performance across the two datasets, showing that a fixed threshold must be tailored to the data at hand and carefully selected. In the MAS dataset, the performance of the three fixed thresholds is in the middle of the range, while in the ADNI dataset, which has a different number of features and samples, the fixed thresholds exhibit both the best and worst performance. It is clear that a fixed threshold must be carefully chosen to suit the dataset under investigation.The features selected by the ensemble feature selectors may be indicative biomarkers for AD. However, because of the stochastic nature of machine learning, even with improved stability each combination of feature selector, aggregator and threshold can still return a slightly different set of features. So, a decision must be made as to which is the optimal set of selected features.Here the optimal features have been identified as those selected in at least half of the top 10 performing models. In the case of the MAS dataset, this metric was restricted further to features selected in at least 80% of the top performing models, as a large number of features were selected. Features that are selected consistently by models that perform well are likely to be important features. Using this method, the features selected using data-driven thresholding from the MAS dataset are described in Table It should be noted that there are significant differences between the ADNI and MAS datasets. MAS is a population-based study which recruited non-demented individuals randomly from the community, while ADNI recruited people with a diagnosis of AD or cognitively healthy individuals as volunteers. There are also differences in the data collected by these studies. ADNI had a strong focus on neuroimaging  and biomarkers from cerebrospinal fluid (CSF) and plasma. In contrast, MRI imaging was performed on only a subset of MAS patients, PET imaging was not performed, no CSF was collected, and fewer blood tests were performed in the baseline waves of MAS. MAS had a stronger focus on medical history and examination, psychological and neuro-psychological assessment, and self-administered lifestyle questionnaires. Therefore, the features selected in the two datasets differ to some extent.The development of a data-driven threshold that can be applied to ensemble feature selectors for both censored and uncensored data is a distinct advantage. A fixed threshold that is appropriate for one data set may be quite inappropriate for another, especially when those datasets vary greatly in terms of the number and type of features they contain. The successful use of a fixed threshold involves testing of a wide range of fixed thresholds during the development of each model and can be quite time consuming.p-value—a statistically relevant threshold, and so its use is highly recommended, particularly with clinical data. The RRA threshold was tested with a range of different p-values, but there was little to differentiate the performance of the different p-values in either dataset.The data-driven thresholds tested here performed well, being amongst the top performing thresholds in both datasets. The RRA threshold has the added advantage that it provides a The disadvantage of the KDE threshold is that it requires that the data contain a large number of irrelevant features to be successful. The KDE threshold performs better in the ADNI dataset than in the MAS dataset. This is because the ADNI dataset contains more features and so it is likely that it also has more irrelevant features.The Best Probe threshold with the mean value aggregator did not perform well with the LASSO or ELASTICNET in either dataset. On investigation it was found that a random probe was often one of the most important features selected by the LASSO, thereby eliminating other truly important features. This could be the result of correlations in the data as the Lasso is known to select one feature at random from a group of correlated features. Further investigation is warranted to clarify this.The Medrank and Threshold algorithms are primarily aggregators, and both output a fixed number of features, set in this case as the mean length of the ranked lists of features. Therefore, varying this number could affect their performance. Despite this, both perform well in these experiments, demonstrating the importance of an effective aggregation strategy.As well as investigating different thresholds, a number of different feature selectors were examined in this work, including simple filters, penalised regression, random forests and boosted models. Although most previous works on ensemble feature selection have used only filters, it is clear from these results that multivariate feature selectors can also benefit by being used in an ensemble.The ADNI dataset exhibited better performance and stability than the MAS dataset, which was to be expected. ADNI is an observational cohort study, where the number of patients with AD was carefully controlled at the start. Not only are there more cases of AD in ADNI, giving it a much lower censoring rate than MAS, but participants with AD at baseline were accepted into the study. In contrast, MAS is a population-based cohort study, where participants were excluded if they had AD at baseline, and so the number of AD cases cannot be controlled and there are fewer cases of AD than in ADNI.In future it would be of interest to train the models on one AD dataset and apply them to another AD dataset with a comparable cohort and set of features, but the aim here was to demonstrate the applicability of the methods to different datasets, albeit in the same area. The methods were applied to a third dataset, which had few events and fewer samples than the other datasets. The results were mixed and have not been included here but they demonstrate the need for an adequate amount of data for these methods to succeed.Future work could investigate the use of the Knockoffs method for variable selection , rather Future work could also apply the techniques developed here to heterogeneous ensembles of feature selectors, where a different feature selector is applied to each sample of the training data. Using different feature selectors would introduce more diversity into the ensemble and so may produce enhanced results.This study has demonstrated the use and validity of data-driven thresholding methods applied to ensemble feature selectors, to provide more stable, and therefore more reproducible, selections of features than individual feature selectors, without loss of performance. Several methods of data-driven thresholding have been used in a novel context and have been shown to perform well. The use of a data-driven threshold eliminates the need to choose a threshold a-priori and can select a more meaningful set of features. A reliable and compact set of features can produce more interpretable models by identifying the factors that are important in understanding a disease  and can A number of multivariate feature selectors were tested for use in feature selection ensembles, in contrast to the univariate filters that are typically employed in this context. Issues arising from the application of the data-driven thresholds to heterogeneous data and multivariate feature selectors, particularly those that select a subset of features rather than returning a score for each feature, were overcome, allowing these methods to be used in feature selection ensembles.The ability to produce more stable selections of features means that clinicians can have more confidence in the results produced by machine learning models. Features that are predictive of Alzheimer's disease have been selected from the models developed here and these are in keeping with findings in the AD literature.