Deduction Algorithm Research Articles

A Novel Conflict Deduction Algorithm Based on Contradiction Separation Inference Rule

Automated reasoning, a significant field within artificial intelligence, has attracted increased attention in recent years due to the rising demand for trustworthy AI. Binary resolution, among other inference rules, is crucial in automated reasoning of first-order logic, including the new conflict resolution method. Conflict resolution processes only two clauses in each deduction step and eliminates a complementary pairs of literals from input clauses. This paper proposes a contradiction separation conflict deduction (CSCD) method based on the contradiction separation rule to address these limitations. This novel resolution methodology, together with its automated reasoning theory and method, handles several clauses in each deduction step to seek for conflicts and generates learnt clauses through synergized deduction. Thus, the approach improves deduction by detecting conflicts more effectively, especially with lengthier input clauses. CSCD and conflict resolution are analyzed in detail, then how to create a practical CSCD algorithm and its implementation is summarized. We tested the CSCD algorithm to solve the CASC-26 problems and also applied it to the current leading ATP system (Eprover). Experimental results show that the CSCD deduction approach improves reasoning capability of conflict deduction method. Additionally, the Eprover with the proposed CSCD algorithm improves its performance and has solved various problems with a rating of 1 from the benchmark database TPTP.

Pneumonia Detection through Deep Learning: A Comparative Exploration of Classification and Segmentation Strategies

The Convolution Neural Network (CNN) algorithm is one of the most widely used methods for identifying and categorizing lung cancer. This paper covers the most suitable architecture and CNN algorithms for lung cancer and pneumonia deduction and classification. The main contributions to the diagnosis and classification of lung cancer with four steps are Nonlinear transfer learning framework (NLTF), Hierarchical Feature Mapping (HFM), Lifelong Partial Dissection (LPD), and Deep Lifelong Convolutional Neural Network (DLCNN). The application of non-local total fuzzy (NLTF) filtering removes various categories of noise after lung CT imageries and enhances cancer areas. The application of Hybrid Fuzzy Morphology (HFM) constructed segmentation to minimize the region of interest (ROI) for cancer using morphology opening and closing processes. Extraction of traits unique to each disease employing Lung Parenchyma Division (LPD) and extraction of deep seismic features using the Geometric Optimal Algorithm (GOA). Training and testing the proposed Deep Learning Convolutional Neural Network (DLCNN) model using the extracted features to classify benign, malignant lung cancers and Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia.

Immunocapture of cell surface proteins embedded in HIV envelopes uncovers considerable virion genetic diversity associated with different source cell types.

HIV particles in the blood largely originate from activated lymphocytes and can overshadow variants which may be expressed from other cell types. Investigations of virus persistence must be able to distinguish cells refractory to viral clearance that serve as reservoirs. To investigate additional cell types that may be associated with in vivo HIV expression we developed a virus particle immunomagnetic capture method targeting several markers of cellular origin that become embedded within virion envelopes during budding. We evaluated the ability of markers to better distinguish cell lineage source subpopulations by assessing combinations of different antibodies with cell-sorted in vitro culture and clinical specimens. Various deductive algorithms were designed to discriminate source cell lineages and subsets. From the particle capture algorithms, we identified distinct variants expressed within individuals that were associated with disparate cellular markers. Among the variants uncovered were minority-level viruses with drug resistance mutations undetected by sequencing and often were associated with markers indicative of myeloid lineage (CD3-/CD10-/CD16+ or /CD14+, and CD3-/CD16-/CD14-/CD11c+ or /HLA-DR+) cell sources. The diverse HIV genetic sequences expressed from different cell types within individuals, further supported by the appearance of distinct drug-resistant variants, highlights the complexity of HIV reservoirs in vivo which must be considered for HIV cure strategies. This approach could also be helpful in examining in vivo host cell origins and genetic diversity in infections involving other families of budding viruses.

Comparison of tranexamic acid plus intramuscular oxytocin with intramuscular oxytocin alone for prophylaxis of primary postpartum haemorrhage in vaginal delivery

In contemporary obstetrics, postpartum hemorrhage is one of the primary causes of maternal mortality. Postpartum hemorrhage is defined as blood loss of more than 500 mL within the first 24 hours of birth. The term, late postpartum hemorrhage is used when the bleeding lasts more than 24 hours. Due to the physiological changes that occur during pregnancy, the body may sustain a 500 mL blood loss without experiencing any severe negative consequences. However, even a modest amount of blood loss might be harmful in cases of comorbidities like anemia. However, even a modest amount of blood loss might be harmful in cases of comorbidities like anemia. Postpartum hemorrhage poses a double threat because it decreases the mother's strength and immunity, leaving her more vulnerable to puerperal illnesses. Second, the loss of blood could be fatal. Maternal death occurs gradually as a result of constant trickle-like blood loss. There are predictors which may help us in scrutinizing patients to be labeled as high risk for postpartum hemorrhage, however, there are no formulized criteria for postpartum hemorrhage. The active management of the third stage of labor is a management protocol made to prevent as well as manage this potentially life-threatening condition. It has significantly led to a reduction in maternal mortality rates. However, with more permutation - combinations of medical management and using different pharmacological agents we can devise a better algorithm for further deduction in the mortality rates. Here we propose to randomly allocate and administer an additional drug, tranexamic acid, in addition to uterotonics prophylactically given during the active management of the third stage of labor in vaginal deliveries. In doing so we compare the differences between groups in which only oxytocin was given and in which oxytocin plus tranexamic acid was given.

A complementary ratio based clause selection method for contradiction separation dynamic deduction

Automated reasoning is a key research area of Artificial Intelligence (AI) and has attracted much greater attention in recent years due to the demands of trustworthy AI, where binary resolution inference rule based first-order automated reasoning play a crucial role. Recently, a novel multi-clause dynamic standard contradiction separation (S-CS) inference rule and related automated deduction theory were proposed to overcome the limitations of binary resolution-based automated deduction. Now that strategies for dynamic clause selection are essential for S-CS based automated deduction, in the present work, a class of novel clause selection strategies, along with the corresponding novel complementary ratio based dynamic S-CS automated deduction algorithm (called CRA), are proposed. Furthermore, we are interested in determining whether CRA may be deployed on top of the current best first-order automated reasoning system designs to increase their performance. As a result, CRA is applied to the current leading systems, Vampire and E, resulting in two integrated automatic reasoning systems, CRA_Vampire and CRA_E. Experiment results demonstrate that CRA can improve E and Vampire in CASC 2020–2022 competitions. The new integrated automated reasoning systems can resolve 44 problems with rating of 1, meaning they are intractable by other existing first-order automated systems.

A new type of generic, self-evolving and efficient automated deduction algorithm based on category theory

<abstract><p>In this article, a new type of generalized, self-evolving and efficient automated statement proof algorithm based on new data structures, i.e., brackets and map graphs, and new algorithms is presented. The brackets structure provides an elegant low-knowledge representation of mathematical concepts. The map graphs offer an efficient machine-learning method which let the computer learn knowledge while proving. Additionally, the new finding is built completely on category theory. Furthermore, a prototype of the program is presented and examined for performance.</p></abstract>

An efficient contradiction separation based automated deduction algorithm for enhancing reasoning capability

Automated theorem prover (ATP) for first-order logic (FOL), as a significant inference engine, is one of the hot research areas in the field of knowledge representation and automated reasoning. E prover, as one of the leading ATPs, has made a significant contribution to the development of theorem provers for FOL, particularly equality handling, after more than two decades of development. However, there are still a large number of problems in the TPTP problem library, the benchmark problem library for ATPs, that E has yet to solve. The standard contradiction separation (S-CS) rule is an inference method introduced recently that can handle multiple clauses in a synergized way and has a few distinctive features which complements to the calculus of E. Binary clauses, on the other hand, are widely utilized in the automated deduction process for FOL because they have a minimal number of literals (typically only two literals), few symbols, and high manipulability. As a result, it is feasible to improve a prover’s deduction capability by reusing binary clause. In this paper, a binary clause reusing algorithm based on the S-CS rule is firstly proposed, which is then incorporated into E with the objective to enhance E’s performance, resulting in an extended E prover. According to experimental findings, the performance of the extended E prover not only outperforms E itself in a variety of aspects, but also solves 18 problems with rating of 1 in the TPTP library, meaning that none of the existing ATPs are able to resolve them.

Fully reusing clause deduction algorithm based on standard contradiction separation rule

An automated theorem proving (ATP) system's capacity for reasoning is significantly influenced by the inference rules it uses. The recently introduced standard contradiction separation (S-CS) inference rule extends binary resolution to a multi-clause, dynamic, contradiction separation inference mechanism. The S-CS rule is used in the present work to provide a framework for fully clause reusing deductions. Accordingly, a fully reusing clause deduction algorithm (called the FRC algorithm) is built. The FRC algorithm is then incorporated as an algorithm module into the architecture of a top ATP, Vampire, creating a single integrated ATP system dubbed V_FRC. The objective of this integration is to enhance Vampire's performance while assessing the FRC algorithm's capacity for reasoning. According to experimental findings, V_FRC not only outperforms Vampire in a variety of aspects, but also solves 46 problems in the TPTP benchmark database that have a rating of 1, meaning that none of the existing ATP systems are able to resolve them.

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.

Accurate early-stage colorectal cancer detection through analysis of cell-free circulating tumor DNA (ctDNA) methylation patterns.

3606 Background: Colorectal cancer (CRC) screening programs suffer from poor uptake and biomarkers have limited diagnostic accuracy. The measurement of the methylation status of tumor-derived cell-free DNA in plasma may address these challenges. We used a targeted methylation panel, tumor-derived signal deduction and machine learning algorithm to refine a blood test for the detection of early-stage CRC. Methods: This was a prospective, international multicenter observational cohort study. Plasma samples were collected either prior to a scheduled colonoscopy as part of standard colorectal cancer screening or prior to colonic surgery for primary CRC. Differentially methylated regions (DMRs) were initially selected by analyzing CRC and control tissue samples with whole genome bisulfite sequencing. A targeted sequencing assay was designed to capture these DMRs in plasma ctDNA. Individual sequencing reads were evaluated for cancer-specific methylation signal and scores calculated for each DMR in a sample. A panel of methylation scores originating from 203 DMRs was used in a prediction model building and validated in a test cohort of patients. Results: Calculated scores were used to train a machine learning model on 68 ctDNA samples from 18 early stage (I-II) and 16 late-stage (III-IV) CRC patients and 34 age, BMI, gender and country of origin matched neoplasia-free controls (median age 63 [50-74], mean BMI 27 [19.5-37], female 50%, Spanish and Ukrainian population, distal cancers 50%). This model was then applied to an independent set of subjects from Spanish, Ukraine and Germany, including 36 stage I-IV cancer patients (median age 61.5 [55-82], BMI 28 [16-39], female 47%, 42% of the tumors were distal) and 159 age and sex matched controls. 87 of the control patients had a negative colonoscopy finding (cNEG), 19 had hyperplastic polyps (HP), 37 had small non-advanced adenomas (NAA) and 16 were diagnosed with other benign gastrointestinal diseases (GID). The model correctly classified 92% (33/36) of CRC patients. Sensitivity per cancer stage ranged from 83% (5/6) for stage I, 92% (11/12) for stage II, 92% (12/13) for stage III to 100% (5/5) for stage IV. Specificity of the model was 97% (154/159), with 100% (37/37) NAA, 94% (15/16) GID, 95% (18/19) HP and 97% cNEG patients correctly identified. Lesion location, gender, BMI, age and country of origin were not significantly correlated to prediction outcome. Conclusions: Methylation sequencing data analyzed using read-wise scoring approach combined with a machine-learning algorithm is highly diagnostic for early-stage (I-II) CRCs (89% sensitivity at 97% specificity). This method could serve as the basis for a highly accurate and minimally invasive blood-based CRC screening test with significant implications for the clinical utility of ctDNA in early-stage cancer detection.

A multi-clause dynamic deduction algorithm based on standard contradiction separation rule

In the past decades, automated theorem proving (ATP) for first-order logic has made good progress, in which binary resolution inference rule plays a crucial role. However, as shown in the latest benchmark library of the ATP system, there are still many practical problems that have not been resolved or cannot be effectively resolved. Recently, in order to overcome the limitations of ATP based on binary resolution inference rules, a novel multi-clause dynamic standard contradiction separation (S-CS) inference rule and its automated deduction theory have been proposed. Based on this theory, this paper first clarifies the generality of this S-CS rule by comparing it with some well-known variants of the binary resolution rule, and then focuses on how to design a specific and effective algorithm along with search strategies to realize the S-CS based deductive theory with its implementation. Specifically, the present work proposes a novel S-CS dynamic deduction algorithm (in short SDDA) based on different strategies and summarizes its implementation procedures. In addition, we focus on evaluating whether SDDA, as a novel perspective multi-clause dynamic automatic deduction algorithm, can be applied on top of the current leading ATP system architectures to further improve their performances. Therefore, SDDA is applied to the current leading first-order ATP systems, i.e., Vampire and E, respectively forming two integrated APT systems, denoted as SDDA_V and SDDA_E. Then the capabilities of SDDA_V and SDDA_E are evaluated on the latest benchmark database TPTP, such as the CASC-J9 problems (FOF division) as well as the hard problems with a rating of 1 in the TPTP benchmark database. The experimental results show the effectiveness of SDDA: SDDA_V outperforms Vampire itself, and SDDA_E, outperforms E itself, and the two improved ATP systems have solved a number of hard problems with the rating of 1 in TPTP, that is, some problems in the latest benchmark database TPTP which have not yet been solved by other current first-order ATP systems.

МОДЕЛИРОВАНИЕ И ПРОГНОЗИРОВАНИЕ ИНДИКАТОРОВ СИСТЕМЫ УПРАВЛЕНИЯ В РЕСПУБЛИКЕ МАРИЙ ЭЛ

The emerging clear tendency of the reduction in the number of employees of state authorities and the system of local self-government indicates the decrease in the attractiveness of this area of activity for young personnel. It is advisable to start the analysis of the local government system development by studying its structure at the municipalities’ level of the region. The author uses the cluster analysis methods, which are the main components implementing the inductive and deductive research algorithms, adaptive forecasting methods based on exponential smoothing, autoregression, and integrated running aggregate techniques. The paper presents the classification of the Mari El municipalities according to the indicators of the local self-government system development, specifies the least and the most numerous clusters, and gives their characteristics. The author formed aggregate indicators influencing the average monthly salary of civil servants and municipal employees of local government structures. Classification of municipalities in the region shows that most districts are represented by the average wage of employees of the governing authorities. Considering the fact that the Mari El Republic is characterized by low wage levels compared to other subjects of the Volga Federal District, the study allows concluding that the level of remuneration of employees of the local government system is a key factor determining the attractiveness of this sphere in the labor market of municipalities of the region. Formed aggregated indicators at the level of the Republic, as a whole, show the maximum impact of urban indicators on the overall development of local governing structures.

In Situ Determination of Nitrate in Water Using Fourier Transform Mid-Infrared Attenuated Total Reflectance Spectroscopy Coupled with Deconvolution Algorithm.

Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy has been used to determine the nitrate content in aqueous solutions. However, the conventional water deduction algorithm indicated considerable limits in the analysis of samples with low nitrate concentration. In this study, FTIR-ATR spectra of nitrate solution samples with high and low concentrations were obtained, and the spectra were then pre-processed with deconvolution curve-fitting (without water deduction) combined with partial least squares regression (PLSR) to predict the nitrate content. The results show that the typical absorption of nitrate (1200−1500 cm−1) did not clearly align with the conventional algorithm of water deduction, while this absorption was obviously observed through the deconvolution algorithm. The first principal component of the spectra, which explained more than 95% variance, was linearly related to the nitrate content; the correlation coefficient (R2) of the PLSR model for the high-concentration group was 0.9578, and the ratio of the standard deviation of the prediction set to that of the calibration set (RPD) was 4.22, indicating excellent prediction performance. For the low-concentration group model, R2 and RPD were 0.9865 and 3.15, respectively, which also demonstrated significantly improved prediction capability. Therefore, FTIR-ATR spectroscopy combined with deconvolution curve-fitting can be conducted to determine the nitrate content in aqueous solutions, thus facilitating rapid determination of nitrate in water bodies with varied concentrations.

The Compassionate Computer: Algorithms, Sentencing, and Mercy

Sentencing scholarship largely neglects the possibility of applying algorithms to mercy. This ‎doesn’t come as a surprise: Is there any greater contradiction than between algorithmic decision-‎making and the compassionate, human and interpersonal nature of mercy? Such polarity brings ‎some theorists and policy makers to reject algorithm-based sentencing altogether. In this chapter, ‎we offer a preliminary attempt at integrating mercy within algorithmic sentencing. First, we ‎distinguish between two main concepts of mercy – justice and pure – and different types of ‎algorithms – deductive and inductive. Second, we argue: (a) As long as justice mercy can be ‎reduced to a proportionality-related calculus (e.g., extra harsh suffering) it can be introduced ‎through a deductive algorithm; (b) Pure mercy, being unpredictable, and deviating from justice, ‎can be incorporated mainly through an inductive algorithm. This is true, at least to some extent, ‎even for theories that permit deviation from equality when exercising mercy.‎

Capturing associations in graphs

This paper proposes a class of graph association rules, denoted by GARs, to specify regularities between entities in graphs. A GAR is a combination of a graph pattern and a dependency; it may take as predicates ML (machine learning) classifiers for link prediction. We show that GARs help us catch incomplete information in schemaless graphs, predict links in social graphs, identify potential customers in digital marketing, and extend graph functional dependencies (GFDs) to capture both missing links and inconsistencies. We formalize association deduction with GARs in terms of the chase, and prove its Church-Rosser property. We show that the satisfiability, implication and association deduction problems for GARs are coNP-complete, NP-complete and NP-complete, respectively, retaining the same complexity bounds as their GFD counterparts, despite the increased expressive power of GARs. The incremental deduction problem is DP-complete for GARs versus coNP-complete for GFDs. In addition, we provide parallel algorithms for association deduction and incremental deduction. Using real-life and synthetic graphs, we experimentally verify the effectiveness, scalability and efficiency of the parallel algorithms.

Digital Instantaneous Frequency Measurement of a Real Sinusoid Based on Three Sub-Nyquist Sampling Channels

Multichannel sub-Nyquist sampling is an efficient technique to break through the limitation of the Nyquist sampling theorem for the wideband digital instantaneous frequency measurement (DIFM) receiver. The significant challenge is calculating the folding frequency and solving the ambiguity quickly and accurately. Usually, the researchers adopt a fast Fourier transform (FFT) to calculate the folding frequency and a Chinese remainder theorem (CRT) to solve the ambiguity caused by undersampling. However, these algorithms have the drawback of long response time. In this paper, we use a frequency deduction algorithm based on the total least-squares estimation to measure the folding frequency accurately within a few clocks. We propose a frequency-band division algorithm by dividing the measurable frequency range into a few sub-bands to solve the frequency ambiguity. This deblurring algorithm is more efficient and faster than CRT. We analyse the performance of our algorithms by numerical simulations and functional hardware simulations and compare them with FFT and CRT. We also conduct experiments on a hardware platform to confirm the effectiveness of our algorithms. The simulation results show that our algorithms have better performance than FFT and CRT in accuracy and response time. Board-level measurement results verify that the proposed DIFM technique can capture the frequency from 0 to 5,000 MHz with a maximum of 0.8 MHz root-mean-squared error in less than 200 ns.

Comparison of analytical and difference sensitivity methods for structural damage identification

The sensitivity-based damage identification method receives much attention due to its ability to identify structural damages at element level. This paper aims to discuss the influence of two different deduction algorithms of sensitivity matrix, the analytical method and the numerical difference method, on the parameter identification results of structures with the same identification index. In order to compare the difference between the two sensitivity-based damage identification methods, the frequency-domain mode shape and the time-domain acceleration response information are used as the updating objectives, respectively. And then, the sensitivity matrices of the updating objectives to the same identification index, structural element stiffness parameters, are respectively derived based on the analytical method and the numerical difference method. Finally, a simply supported box girder bridge is used to illustrate the difference between the two sensitivity-based methods. Numerical simulations with initial model errors and measurement noise show the two types of methods both can accurately detect local damages and identify unknown initial model errors. The presence of random noise has some bad effect on the identified results of structural parameters, but the identified accuracy is still acceptable for normal noise level.

KENOTOMY, Creativity Face to Face with Artificial Intelligence

Artificial Intelligence wins an increasing capacity for undertaking more efficiently repetitive jobs and routine based tasks, using highly accurate logic-adaptive and deductive algorithms, replacing successfully the humans for these occupations.Facing this new reality the ultimate asset of mankind became the Creative Thinking.Enhancing and fostering the Creative Thinking should be a major duty especially along the educational process. Whatever the domain of study might be, the ability of generating fresh ideas and disruptive concepts will be sooner than expected the winning trump, or even the only reason for employing a human operator.What is Kenotomy?Kenotomy is Creativity focused education, leading students toward a seminal thinking mode, by developing their genuine imagination talent and enabling them with the skills for shifting approaches, encouraging unusual thinking pathways, archetypal perspective and serendipity, facilitating conceptualizing and recognizing the potential of ideas.Kenotomy is based on the integrative understanding of the creative mental processes and uses an adequate methodology for addressing them, triggering the ideation process.Kenotomy builds up creative minds, whatever the major of the students might be.How does Kenotomy work?Kenotomy differentiates two types of Creativity: the Reactive and the Intrinsic one and describes the dedicated ways for initiating them specifically.First of all Kenotomy relieves the acquired and inherent inhibitions, releasing and stimulating the inborn speculative talent and enhances the ideation ability, overcoming the empiric myopia by high abstracting skills and the routine saturation by a fresh apex-perspective. As a project centered education Kenotomy develops individual disruptive thinking as well as the group synergetic resonance with spectacular outcome: original problem solutions and unexpected, seminal concepts.

CSE_E 1.0: An Integrated Automated Theorem Prover for First-Order Logic

First-order logic is an important part of mathematical logic, and automated theorem proving is an interdisciplinary field of mathematics and computer science. The paper presents an automated theorem prover for first-order logic, called C S E _ E 1.0, which is a combination of two provers contradiction separation extension (CSE) and E, where CSE is based on the recently-introduced multi-clause standard contradiction separation (S-CS) calculus for first-order logic and E is the well-known equational theorem prover for first-order logic based on superposition and rewriting. The motivation of the combined prover C S E _ E 1.0 is to (1) evaluate the capability, applicability and generality of C S E _ E , and (2) take advantage of novel multi-clause S-CS dynamic deduction of CSE and mature equality handling of E to solve more and harder problems. In contrast to other improvements of E, C S E _ E 1.0 optimizes E mainly from the inference mechanism aspect. The focus of the present work is given to the description of C S E _ E including its S-CS rule, heuristic strategies, and the S-CS dynamic deduction algorithm for implementation. In terms of combination, in order not to lose the capability of E and use C S E _ E to solve some hard problems which are unsolved by E, C S E _ E 1.0 schedules the running of the two provers in time. It runs plain E first, and if E does not find a proof, it runs plain CSE, then if it does not find a proof, some clauses inferred in the CSE run as lemmas are added to the original clause set and the combined clause set handed back to E for further proof search. C S E _ E 1.0 is evaluated through benchmarks, e.g., CASC-26 (2017) and CASC-J9 (2018) competition problems (FOFdivision). Experimental results show that C S E _ E 1.0 indeed enhances the performance of E to a certain extent.

Efficient hash function–based duplication detection algorithm for data Deduplication deduction and reduction

SummaryData Deduplication is the foremost technique used for data compression by removing redundant data. It is broadly utilized for cloud storage server, which reduces the un‐utilized memory space and optimized bandwidth. The confidentiality of sensitive information maintains during Deduplication detection. The Efficient Hash Function–based Duplication Detection (EHFDD) algorithm is proposed to encrypt the content before outsourcing in cloud environment. This paper is an attempt to enhance the data security and notify the issues of approved data Deduplication detections. Furthermore, it also presents several novel Deduplication improvisations for helping certified duplicate verification in hybrid cloud. The EHFDD method enables duplicate detection scheme with minimal overhead compared to existing functions. Based on experimental evaluations, proposed EHFDD algorithm reduces the average delay in 28.7 milliseconds, memory utilization in 8%, computation time in 457 milliseconds, communications overhead in 900 milliseconds, and improves the success rate in 3.13%.