Client Data Research Articles

An Effective Research on Data Mining Techniques for Intrusion Detection & Learning Classes

It exhibits that the accumulation based alignment is altogether introduced mentioned than normal unmarried-model strategies; supervised acquirements beats unsupervised learning, and accretion the amount of apocryphal negatives connects to university precision. It demonstrates activity over consequences facts. For alignment facts, this cardboard proposes and checks an unmonitored, association primarily based acquirements including that keeps up a organized chat advertence of addled successions observed. All through able abstracts surges of great breadth to analyze inconsistencies. In unsupervised learning, burden based techniques are activated to archetypal basal conduct groupings. This outcomes in a classifier announcement a ample accession in alignment attention for abstracts streams absolute cabal blackmail irregularities. This accouterments of classifiers allows the unsupervised manner to accord with exhausted established changeless acquirements methodologies and lifts the functionality over supervised acquirements methods. One of the bottlenecks to frame backpack chat advertence is adaptability. For this, a executed adjustment is proposed and done utilizing Hadoop and Map lessen machine.We could augment the plan CRISP-DM archetypal in the accompanying means To activate with, we will assemble an actual framework to bolt applicant addition as beck utilizing apache abysm and abundance it on the Hadoop conveyed certificate framework (HDFS) and afterwards that administer our methodologies. Next, we will administer Map Reduce to amount adapt abolish amid examples for a specific client's adjustment assumption data.

An Deliberate Decentralized File Duplication Algorithm In Sharing System

of document is a technology that is used in peer to peer sharing networks. This technology is used to lessen center spots and enhance record utilization performance. Maximum modern record to fold back strategies mirror documents in every node else two give client and server data request direction. Those strategies may have less potency or with a cost of high overload. To fold back files in server, this aspect intensify duplicate hook price, so, research logicality however fabricate encumber nodes and cannot extensively lessen query route duration. File replication in consumer aspect ought to substantially lessen query course duration, however cannot guarantee excessive reproduction hit price to absolutely make use of replicas. Though facsimile on the edge of question route interpret troubles, as far as excessive fee of aloft to greater facsimile and produces less utilized facsimile. Peer networking communication is an architecture that occurs throughout an application that divides loads among peers. Neighbor’s nodes are similarly classified efficacy contributors inside utility. Peer nodes are to shape seem-to-neighbor nodes in a network. Next node makes a element in their resources, consisting of processing strength, disk garage or community bandwidth, immediately ready to rest community members, no need for relevant to set in order through reliable hosts.

Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the real-world domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy.

What if you ask and they say yes? Consumers' willingness to disclose personal data is stronger than you think

Technological progress—including the development of online channels and universal access to the internet via mobile devices—has advanced both the quantity and the quality of data that companies can acquire. Private information such as this may be considered a type of fuel to be processed through the use of technologies, and represents a competitive market advantage. This article describes situations in which consumers tend to disclose personal information to companies and explores factors that encourage them to do so. The empirical studies and examples of market activities described herein illustrate to managers just how rewards work and how important contextual integrity is to customer digital privacy expectations. Companies' success in obtaining client data depends largely on three Ts: transparency, type of data, and trust. These three Ts—which, combined, constitute a main T (i.e., the transfer of personal data)—deserve attention when seeking customer information that can be converted to competitive advantage and market success.

Willingness to Use Mobile based e-Partograph and Associated Factors among Care Providers in North Gondar Zone, Northwest Ethiopia.

The proper use of Partograph supports to capture key maternal and fetal data. Paper-based Partograph are prone to error, incompleteness, delayed decisions and loss of clients' information. Electronic (e-Partograph) enables to easily retain and retrieve client data to ensure quality of care. Mobile technologies found an opportunity for resource-limited countries to improve access and quality of health care. Evidences were lacking on end users' acceptance to e-Partograph. This study aimed to assess obstetric care providers' willingness to use mobile based e-Partograph and its associated factors. Institutional based cross-sectional study was conducted from December 30, 2016 to January 21, 2017. A total 466 obstetric care providers were selected using multistage sampling technique in North Gondar Zone, Northwest Ethiopia. A structured self-administered questionnaire was used to collect the data. The data were entered in to Epi info version 7 and analyzed by using SPSS version 20. Cronbach's Alpha test was calculated to evaluate the reliability of data. A multivariable logistic regression analysis were used to identify factors associated with dependent variable. Adjusted odds ratio with 95%CI was used to determine the presence of association. The study found that 460(99.6%) of care providers owned mobile phone. Smartphone owners accounted only 102(22%). Of them, 205(46%) were willing to use mobile-phone for e-Partograph. Care providers aged >30 years (AOR=2.85, 95% C.I: 1.34-6.05), medical doctors and higher level clinicians (AOR=8.35, 95% C.I: 2.07-33.63), Health Center (AOR=4.41, 95% C.I:0.10-9.26), favorable attitude towards Partograph (AOR=2.76, 95% C.I: 1.49-5.09) and related in-service trainings (AOR=7.63, 95% C.I: 3.96-14.69) were enabling factors for willingness to use mobile phone. Almost all obstetric care providers had access to mobile phone, however; smartphone ownership is still low. Willingness to use mobile-phone for e-Partograph was low. Younger aged, lower level clinicians, Hospital based workers, unfavorable attitude on Partograph and lack of in-service trainings were main factors for non-willingness. Hence awareness creation on partograph use and digital capacity building are crucial for effective e-partograph management.

Understanding Advertising Client–Agency Relationships in China: A Multimethod Approach to Investigate Guanxi Dimensions and Agency Performance

This study examines how guanxi (relationships, connections) between advertising agencies and their clients impact their creative performance and implementation by integrating both Chinese guanxi and Western relationship quality constructs into a conceptual framework. Using a multimethod approach, Study 1 and Study 2 used surveys to examine the influence of the three guanxi aspects—ganqing (emotional bonding, affection), renqing (exchange of favors), and xinren (interpersonal trust)—on agency performance from both agencies’ and clients’ perspectives. Study 3 used dyadic interviews to gather qualitative insights on advertising client–agency relationships in China. Results show similar patterns in the relationships between the three guanxi dimensions and agency performance in both agency and client data sets. Specifically, ganqing was found to associate positively with cooperation in both data sets. The impact of renqing on cooperation was significant in the agency data but not in the client data. On the other hand, xinren was found to contribute positively to cooperation in the client data but not in the agency data. In both data sets, cooperation was positively related to satisfaction, which leads to agency creativity and the implementation of advertising campaigns in China. The results of this study are important for further understanding the Chinese advertising market and providing insights on how guanxi influences agency performance across Asian countries.

217 Investigating the Delivery of Falls Prevention Interventions to Older People Following Multi-Factorial Risk Assessment: A Cross-sectional Study

Abstract Background Falls are one of the most common threats to older peoples’ independence. In Ireland, approximately one in three adults aged ≥65 years fall each year. Multifactorial interventions, which include an assessment of an individual's risk of falling followed by customised interventions or referral have been shown to reduce the rate of falls among community-dwelling older people. As part of an Integrated Falls Prevention Pathway initiated in 2016, six multidisciplinary risk assessment clinics were established in Cork city and county. The aim of this study is to examine whether recommended follow-on interventions were received following a falls risk assessment in the community. Methods Routinely collected administrative data for clients who attended a falls risk assessment clinic are being collated. Data include client demographics, onward referrals, waiting times and receipt of intervention. A process map of the patient pathway following a falls risk assessment is under development and will be refined based on the study findings. Results Preliminary analysis of a two-year implementation period (April 2016-2018) indicates that following assessment, clients received an average of 2.4 onward referrals. Most referrals were made to general practice (29%, n=315), community physiotherapy (25%, n=272), and community occupational therapy (15%, n=165). Other referrals were to public health nurses (n=104, 10%) and falls prevention classes (n=60, 6%). Further analysis will identify the percentage of individuals who receive interventions, the type of interventions received, the percentage of patients who do not attend and the waiting lists. Conclusion The Integrated Falls Prevention Service is the sole example of an operational integrated falls pathway in Ireland. This research will identify potential roadblocks for providers and clients along the pathway and will identify opportunities to. The results will also be used to inform service planning and resource allocation to ensure that this model of care is sustainable.

A Three Layer Privacy Protective Cloud Storage Theme Supported Procedure Intelligence in Fog Computing

With the dangerous development of unstructured data, distributed storage innovation gets a great deal of consideration and higher advancement. Nonetheless, in current stockpiling pattern, client's data is totally held in cloud servers. In various words, clients lose their privilege of the executives on data and face security departure hazard. Old security assurance plans square measure now and again upheld encoding innovation, anyway these assortments of systems can't successfully oppose assault from the cloud server. To determine this downside, have a will in general propose a three-layer stockpiling system upheld haze figuring. The arranged system will each exploit distributed storage and shield the protection of information. Moreover, Hash-Solomon code equation is intended to isolate data into totally various components. At that point, we can put a little low a piece of data in local machine and mist server to shield the security. In addition, upheld process insight, this equation will figure the appropriation extent held in cloud, mist and local machine, severally. Through the hypothetical wellbeing examination and exploratory investigation, the practicality of our subject has been substantial, that is fundamentally a hearty enhancement to existing distributed storage topic is the observer of the ongoing years distributed computing innovation.

Elliptic Curve based Collaborative Group Key Management for Cloud Data Sharing in Ciphertext Policy Attribute based Encryption

Ciphertext policy attribute based encryption (CPABE) is one of cryptographic procedure that coordinates data information encryption with authenticated admission management for guaranteeing data information security in Cloud. The cloud computing where the application administrations using the Internet. In numerous applications, especially in Group Communication in cloud data information offering to various clients data information proprietors require to conceal undisclosed data information like encryption key. The effective key management system conventions are essential for giving security to this data information. Since, it is extremely difficult to provide security to group's secret data information particularly with various mechanisms of assurance. The effectiveness issue of CP-ABE is as yet accessible that corrupt the computation overhead on the grounds that bilinear pairing is exorbitant in such huge numbers of utilizations of ABE, so by supplanting the bilinear pairing utilized in the attribute coordinating with straightforward scalar multiplication on Elliptic Curves, accordingly diminishing the general computation just as communication overhead. And plan another key dispersion can considered in order to legitimately renounce a client or a characteristic without refreshing the other clients' keys during the property repudiation phase. By utilizing the Shamir's Threshold secret sharing designs access structure to develop the expressiveness of the entrance arrangement. Security and execution examination demonstrate that in this plan improving the general effectiveness just as guarantee the security of the cloud data information

Multi-Service Programs for Pregnant and Parenting Women with Substance Use Concerns: Women’s Perspectives on Why They Seek Help and Their Significant Changes

Within Canada, several specialized multi-service prevention programs work with highly vulnerable pregnant and early parenting women with substance use issues. Experiences of trauma, mental health, poverty, and other factors associated with the social determinants of health complete the picture. Program evaluations have demonstrated their value, but less has been said as to women’s reasons for choosing to seek help from these programs, what they were hoping to gain, or what difference they believe has occurred as a result. The Co-creating Evidence project is a multi-year (2017–2020) national evaluation of holistic programs serving women at high risk of having an infant with prenatal alcohol or substance exposure. The evaluation uses a mixed methods design involving quarterly program output and “snapshot” client data, as well as in-person, semi-structured interviews and questionnaires with clients, program staff, and program partners. This article presents findings from interviews with women regarding why they sought help, how they used the services, and what they perceived to be the most significant change in their lives as a result. Obtaining help with substance use was the top theme for what women hoped to get from their participation in their program; however, women’s reasons were often intertwined. Additional motivations included wanting information, support or assistance with: child welfare; pregnancy; housing; getting connected to health care or prenatal care; and opportunities for peer support. With respect to the most significant life change, themes included: reduced substance use; improved housing; stronger mother–child connection; and improved wellness and social connections. Findings demonstrated that vulnerable, marginalized pregnant and parenting women who are using substances will seek help when health and social care services are configured in such a way as to take into consideration and address their unique roles, responsibilities, and realities.

Efficient Secure Outsourcing of Large-Scale Convex Separable Programming for Big Data

Big data has become a key basis of innovation and intelligence, potentially making our lives more convenient and bringing new opportunities to the modern society. Towards this goal, a critical underlying task is to solve a series of large-scale fundamental problems. Conducting such large-scale data analytics in a timely manner requires a large amount of computing resources, which may not be available for individuals and small companies in practice. By outsourcing their computations to the cloud, clients can solve such problems in a cost-effective way. However, confidential data stored at the cloud is vulnerable to cyber attacks, and thus needs to be protected. Previous works employ cryptographic techniques like homomorphic encryption, which significantly increase the computational complexity of solving a large-scale problem at the cloud and is impractical for big data applications. For the first time in the literature, we present an efficient secure outsourcing scheme for convex separable programming problems (CSPs). In particular, we first develop efficient matrix and vector transformation schemes only based on arithmetic operations that are computationally indistinguishable both in value and in structure under a chosen-plaintext attack (CPA). Then, we design a secure outsourcing scheme in which the client and the cloud collaboratively solve the transformed problems. The client can efficiently verify the correctness of returned results to prevent any malicious behavior of the cloud. Theoretical correctness and privacy analysis together show that the proposed scheme obtains optimal results and that the cloud cannot learn private information from the client's concealed data. We conduct extensive simulations on Amazon Elastic Cloud Computing (EC2) platform and find that our proposed scheme provides significant time savings to the clients.

UNIQUE METHODOLOGY TO MITIGATE ANTI-FORENSICS IN CLOUD USING ATTACK- GRAPHS

In this challenging era of technology where cloud computing has expanded its application wings in almost all areas. From individual end-users, small scale business to large organizations. All of them are now free from the maintenance, updating the software and taking the advantage of low-cost investments just by hiring the services and required applications from the cloud rather than building and maintaining the same. Though these advantages are creating much demand for cloud services with more required features at the same time, a challenge comes on the other side from attacks on client's data in the cloud. It becomes more difficult for the investigator to carry out the investigation during cyber-attacks on the cloud. During the attacks, there is a need for a quick response when certain anti-forensics is carried out to mislead the investigators. They may lead to waste of time, efforts, and spent cost. This paper presents a unique methodology to mitigate anti-forensics in a cloud with the effective usage of the relevant graphs. With the effective usage of graphs by the generation of attack, evidence and anti-forensic graphs helpful for the investigator to understand the cause and to carry out relatively countermeasures right way without being deviated by the anti-forensics.

An Efficient Data Segmentation and Replication Technique for Cloud using Fuzzy Centrality Measures

Cloud computing is a creating worldview to give reliable and resilient infrastructure permitting the clients (data proprietors) to store their data and the data purchasers (clients) can get to the data from cloud servers. This worldview decreases storage and maintenance cost of the data proprietor. Notwithstanding, cloud data storage still offers ascend to security related issues. In the event of shared data, the data face both cloud-explicit and insider threats. In this work, we propose fuzzy centrality measure based division and replication of data in the cloud for perfect execution and security that consider both security and execution issues. In our framework, we separate a data records and imitate the isolated data over the cloud center points utilizing fuzzy centrality measures. Every one of the nodes stores just a solitary data fragment of a particular data document that guarantees that even if there should arise an occurrence of a fruitful attack, no significant information is uncovered to the attacker. In addition, the cloud nodes storing the data fragments, are separated with certain distance by methods for altered fuzzy T-coloring to prohibit an attacker of predicting the locations of the fragments. We likewise contrast the exhibition of the our methodology and other standard replication plans. The greater amount of security with improved performance was observed.

Fumigating Quality of Service and Data Confidentiality in Cloud with Message Digest 5 Algorithm

With help of cloud stockpiling, data proprietors can remotely store their data without having the weight of neighborhood data stockpiling and upkeep. Be that as it may, this likewise have numerous security concerns. One among them is the uprightness of data put away in the cloud. Shared data in the cloud shows a one of a kind test as assets are conveyed, making it hard to know where the client data is found and who has admittance at a given time. Conventional approach includes a Third Party Auditor (TPA) who recover whole data from cloud for data honesty confirmation by checking the rightness of marks. We propose a system, wherein clients can produce labels before transferring their data. The data is then scrambled utilizing Advanced Encryption Standard calculation and put away in cloud at the supervision of the TPA. The execution of various reviewing assignment concurrent is conceivable. With the assistance of Message Digest 5 calculation, data deduplication is likewise refined alongside secrecy. In our technique, clients just require a solitary key for decoding, which makes framework operations to be efficient paying little respect to the intricacy of framework.

The Arithmetic Of elliptic Curve for Prime Curve Secp-384r1 using One Variable Polynomial Division for Security of Transport Layer Protocol

In this paper, we present a new method for solving multivariate polynomial elliptic curve equations over a finite field. The arithmetic of elliptic curve is implemented using the mathematical function trace of finite fields. We explain the approach which is based on one variable polynomial division. This is achieved by identifying the plane p with the extension of and transforming elliptic curve equations as well as line equations arising in point addition or point doubling into one variable polynomial. Hence the intersection of the line with the curve is analogous to the roots of the division between these polynomials. Hence this is the different way of computing arithmetic of elliptic curve.Transport layer security provides end-to-end security services for applications that use a reliable transport layer protocol such as TCP. Two Protocols are dominant today for providing security at the transport layer, the secure socket layer (SSL) protocol and transport layer security (TLS) protocol. One of the goals of these protocols is to provide server and client authentication, data confidentiality and data integrity. The above goals are achieved by establishing the keys between server and client, the algorithm is called elliptic curve digital signature algorithm (ECDSA) and elliptic curve Diffie-Hellman (ECDH). These algorithms are implemented using standard for efficient cryptography(SEC) prime field elliptic curve secp-384r1 currently specified in NSA Suite B Cryptography. The algorithm is verified on elliptic curve secp-384r1and is shown to be adaptable to perform computation.

An Efficient Data Segmentation and Replication Technique for Cloud using Fruit Fly Optimized Centrality Measures

Cloud computing is a creating worldview to give dependable and versatile framework permitting the clients (data proprietors) to store their data and the data customers (clients) can get to the data from cloud servers. This paradigm decreases stockpiling and upkeep cost of the data proprietor. However, cloud data storage still gives rise to security related problems. In case of shared data, the data face both cloud-specific and insider threats. In this work, we propose FOA( fruit fly optimization algorithm ) optimized centrality measure fragmentation and replication of information in the cloud for optimum performance and security that consider both security and performance issues. FOA is a technique for deducing global optimization based on the foraging character of the fruit fly. The sensory perception of the fruit fly is superior than that of other species, particularly the sense of smell and vision . In our methodology, we divide a data files and replicate the fragmented data over the cloud nodes using FOA centrality measures. Every one of the cloud node just store a single information data fragment that ensures even if there arise an occurrence of a successful attack ,no important information is shown to the attacker .We also compare the performance of the our methodology with other standard replication schemes. Observed results shows higher level of security and performance imrpovements.

A Public Auditing Protocol for Cloud Storage System With Intrusion-Resilience

Cloud storage auditing helps users to check the integrity of their data stored on the cloud. However, if the client's auditing secret key is exposed to the malicious cloud, the client's data may be deleted by the malicious cloud without being detected. In this paper, we propose a public auditing protocol with intrusion-resilience to relieve damage caused by the key-exposure problem. The proposed protocol divides the lifetime of files stored on the cloud into several time periods, and each time period is further divided into several refreshing periods. The auditing secret key is updated in each time period, and secret values used to update the auditing secret key change in every refreshing period. These two update operations are completed by the client and the third party auditor (TPA). This protocol is secure against the adversary as long as the client and TPA are not compromised in the same refreshing period. Security proof under random oracle model proves the protocol is secure, and the experimental results indicate that the performance of the protocol is acceptable.

Managing and Adapting Practice: Provider Perceptions of an Evidence-Informed Framework for Delivering Mental Health Services

This study examined providers' reflections on delivering managing and adapting practice (MAP), an evidence-informed framework that guides decision-making from scientific and client data. Consensual qualitative research methods were used to analyze the reflections of 201 youth mental health providers. Results indicated that providers approached MAP according to their own preferences and particular cases. While most appeared to approach MAP from a practice management standpoint, when faced with challenging cases, providers used coordination and outcomes management resources. Regardless of approach, most providers came to appreciate the full framework through reflective practice. Their diverse approaches offer lessons for evidence-based practice implementation and sustainment.

Does Truth Have a Future?

Does Truth Have a Future?

Adverse events in a large-scale VMMC programme in Tanzania: findings from a case series analysis.

IntroductionAdverse events (AEs) rates in voluntary medical male circumcision (VMMC) are critical measures of service quality and safety. While these indicators are key, monitoring AEs in large‐scale VMMC programmes is not without challenges. This study presents findings on AEs that occurred in eight years of providing VMMC services in three regions of Tanzania, to provide discussion both on these events and the structural issues around maintaining safety and quality in scaled‐up VMMC services.MethodsWe look at trends over time, demographic characteristics, model of VMMC and type and timing of AEs for 1307 males who experienced AEs among all males circumcised in Tabora, Njombe and Iringa regions from 2009 to 2017. We analysed deidentified client data from a VMMC programme database and performed multivariable logistic regression with district clustering to determine factors associated with intraoperative and postoperative AEs among VMMC clients.Results and discussionAmong 741,146 VMMC clients, 0.18% (1307/741,146) experienced a moderate or severe AE. The intraoperative AE rate was 2.02 per 100,000 clients, and postoperative rate was 2.29 per 1000 return clients. Multivariable logistic regression showed that older age (20 to 29 years) was significantly associated with intraoperative AEs (aOR: 3.51, 95% CI: 1.17 to 10.6). There was no statistical significant difference in AE rates by surgical method. Mobile VMMC service delivery was associated with the lowest risk of experiencing postoperative AEs (aOR:0.64, 95% CI: 0.42 to 0.98). AE rates peaked in the first one to three years of the programme and then steadily declined.ConclusionsIn a programme with robust AE monitoring methodologies, AE rates reported in these three regions were very low and declined over time. While these findings support the safety of VMMC services, challenges in reporting of AEs in a large‐scale VMMC programme are acknowledged. International and national standards of AE reporting in VMMC programmes are clear. As VMMC programmes transition to national ownership, challenges, strengths and learning from AE reporting systems are needed to support safety and quality of services.