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Bones are living tissues made of calcium and phosphorus among other elements. In the early years, they replenish themselves and expand quickly. The bone is viewed as a homogenous, isotropic, linear-elastic material. The foundation of the human skeleton are the bones. It aids in supporting the body's softer areas. Trauma is a significant factor in both industrialised and developing nations in terms of mortality and disability. According to the World Health Organization (WHO), trauma will account for the majority of years of life lost in both developed and developing countries by the year 2020. One of the frequent traumas is a bone fracture. Using bone plates to connect the fractured bone is one way to treat the broken bone. This study compares bone plates constructed of several biomaterials (Stainless Steel, Titanium, Alumina, Nylon, and PMMA) to determine which one is the best. SOLIDWORKS is used to model the femur bone, while ANSYS is used for analysis. Also modelled, attached to a fractured bone, and examined are the fracture fixation plates.

It is possible to store data in the cloud in a variety of different ways, including in a single database or across numerous servers; however, database clients are only able to save their information on a single server. No matter how straightforward the management of data in a single database may appear to be, there will invariably be complications regarding concerns of safety, consistency, availability, and bottlenecks. Distributed databases eliminate this issue by splintering the data among different servers, which in turn makes it possible for multiple clients to access the database at the same time. All of this information is readily available to the public and may be viewed whenever one chooses and from wherever one may be. Clients are able to quickly access replicas even in the event that the original is destroyed as a result of concurrent access. Users also have the option of protecting the data using either the Advanced Encryption Standard (AES) or the Triple Data Encryption Standard (3DES). Ensuring the privacy of sensitive data can be accomplished using any one of a number of distinct ciphering techniques, which can then be applied to any one of several distinct data formats. In the event that it is found out that the data was also altered, a procedure for recovering it will be carried out. In the case that a cloud instance experiences an error, the problem is fixed right away. This architecture protects sensitive data by removing potential points of failure from the design. Clients may not be aware of the encryption technology that is used to secure data or the particular cloud server instance that is responsible for delivering it if the system is configured in this manner. Hence, a unified strategy for the architecture of cloud-based databases offers higher levels of security, consistency, and accessibility.

Estimating water quality has existed as one of the vital factors embarked on the planet in the present eons. This paper illustrates a water quality estimate based on the Linear Discriminant Analysis (LDA) technique. Weighted arithmetic index technique is used in the computation of the Water Quality Index (WQI). At that moment, the LDA is linked to the dataset, and the ultimate principal WQI dynamics have been determined. Subsequently after predicting the WQI, Light Gradient Boosted Machine (LGBM) classification is performed in the LDA. Lastly, the LGBM classifier is activated to label the water quality. This proposed LGBM with LDA technique is demonstrated and evaluated on a Gulshan Lake-related dataset. The results show 96% forecast accuracy for the LDA and 100% categorization accuracy for the Light Gradient Boosted Machine classifier system that indicate consistent interpretation linked over the futuristic prototypes. This innovative model LDA-LGBM is aimed at enhancing the prediction of water quality and its classification through AI - ML approach.

A novel conception of Knowledge Lake, i.e., a Contextualized Data Lake is to be soundly educated. The deliberated big-data practices pave a means for the erection of Intelligent Knowledge Lakes and that being the resources for big-data applications and analytics. This analysis likewise opens the welfares, disputes, and exploration prospects of Intelligent Knowledge Lakes. Data Science is launched as an influential discernment through businesses. Organizations today are dedicated on transforming their facts into ultra-practical intuitions. This work is challenging, as in present day’s intelligence, amenity and cloud customary budget trades accumulate immense aggregates of unprocessed data after a variety of funds. Data Lakes are familiar as a packing depository that fetch concurrently the unprocessed data in its innate set-up (sustaining to NoSQL from relational databases) which is crucial. The logic behind Data Lake is to stockpile unprocessed data and let the data analyst resolve the way to curate them well ahead of reviewing the idea of Knowledge Lake, which is an anecdotal Data Lake. The Intelligent Knowledge Lake stipulate the basis for big data analytics by robotically curating the unprocessed data in the Data Lake grooming these for stemming intuitions via programmed interactive real-time optimized data wrangling in intelligent knowledge lakes. Computerization of an exposed free public Data and Knowledge Lake amenity provides developers and researchers a distinct REST API to systematize, curate, catalog and interrogate their data and metadata in the Lake for a longer time. It administers manifold database/databank know-hows (from Relational to NoSQL) that deals with an inherent scheme for data security, curation, and provenance.

Cybersecurity is an extensive and vivacious domain in the commercial progression of the ecosphere. By up-to-date inhabitants, networking settings and assets, cybersecurity fits with the exigent task to realize the necessities of the imminent populace. Intelligent cybersecurity / intellectual smart cybersecurity has risen as a pioneering tool to deal with latest ambiguities in programmed cybersecurity enduring capability by bringing together Artificial Intelligence (AI) in Cybersecurity Computerization. The mechanism that enterprises in this cutting-edge technology handles the mechanism capability to acquire via depleting Bootstrapped Meta-learning and reinforced with rewards as Supreme Cybersecurity vintages, besides least resource utilizations as well as time limits. AI empowered cybersecurity technology is a vital constituent of the imminent cybersecurity revolution ahead. During this operation, a proficient computerization of AI application in the arena of cybersecurity sustenance is ready for attaining the supreme output welfares as results, also inhibiting the real assets. Setting the precise real-time issues are trailed by cracking it for affluence and escalation or magnification of cybersecurity thus by prominent universal preeminent impending cybersecurity. A meta-learning/AI-based automated security strategy is vital in the protection of critical infrastructure, users and assets disinclined to outbreaks.

Malign websites effectively endorse the evolution of web illicit events and force the progression of Web services. As an efficient outcome, there is powerful enthusiasm to create systemic resolutions in inhibiting the client from the call onto such Websites. Knowledge-centered Random Forest outfits with XGBoost tactic is recommended for categorizing Websites into 3 categories: Benign, Spam and Malicious. This practice evaluates the Uniform Resource Locator in the situation deprived of accessing the matter of Websites. Thus, it wipes out the run-time expectation and the likelihood of uncovering clients to the browser aimed susceptibilities. As a consequence of involving Random Forest Ensembles with XGBoost, it realizes superior enactment on expansive view and publicity correlated with blacklisting amenity. Preprocessing is performed in order to improve the quality of the data subsequently, analyze certain algorithms, thereby explore the best model are the facts discussed in this research. Work also continues to probe how well this chosen archetypal will operate in the future ahead.

Agriculture is a noteworthy and vibrant domain in the fiscal evolution of the globe. With populationin progress, climatic situation and assets, and agriculture turn out dazed to be a crucial task to realize the necessities of the future population. Intelligent precision agriculture/intelligent smart farming has transpired as an innovative tool to tackle hovers of the future ahead in automated agricultural sustainability by leading Artificial Intelligence (AI) in agriculture automation.AI unravels critical farm labor challenges by improving or reducing work and lessening the necessity of numerous workers. Agricultural AI aids in reaping harvests quicker than human employees at a greater quantity, further precise in categorizing and eradicating unwanted plants, also dropping cost and menace. This process motivates the cutting-edge technologies capitulating the machine capability to learn by sourcing Bootstrapped Meta-learning also reinforcing with rewards as maximum crop yields and minimum resource utilizations as well as within time limits. AI empowered farm machinery is the key constituent of the future agriculture revolution ahead. In this exploratory work, an efficient automation of AI application in the field of agriculture sustenance is ensured for receipt of the most obtainable aids as outcomes and inhibiting the applied assets. Fixing the precise real-time issues trailed by unravelling it for agricultural augmentation or amplification thereby leads to the global best future agriculture.

A dashboard is a powerful tool that enables users to verify, analyze, and process all kinds of data. It represents information on a single screen that helps get it just whenever it is essential and achieve the set goals in time. The category of a dashboard varies due to the nature of data they signify and the drive of the corporate they are castoff by. To blueprint a dashboard that will encounter the entire necessities and wholly accomplishing its targets, custom the ideologies of worthy dashboard sketch. But functioning on the project task, continually ponder its objective and the gain of the target spectators. Create it plain, operational, functional and spotlight on mandate demands. Ensure not to agree with the blueprint that complicates customers via the content, besides UI that will require a clear base fitting into the context. Group data, make it consistent, and add custom facets if applicable. Optimize them; finally, get feedback from customers to find out what's working perfectly and what is to be amended to mark them even happier. In future this optimized dashboard can be blended with Machine Learning, Deep Learning and Artificial Intelligence for amplifying their performance, reducing their cost and time.

Athletics bureaucrats round the globe are tackling implausible encounters owing to the partial methods of customs executed by the athletes to progress their enactment in their sports. It embraces the intake of hormonal centred remedies or transfusion of blood to upsurge their power and the effect of their coaching. On the other hand, the up-to-date direct test of discovery of these circumstances embraces the laboratory-centred technique viz restricted for the reason that of the cost factors, handiness of medical experts, etc. This ends us to pursue for indirect assessments. By the emergent curiosity of Artificial Intelligence (AI) in healthcare, it is vital to put forward a process built on blood factors to advance decision making. In this research script, a statistical and machine learning (ML) centred tactic was suggested to ascertain the concern of doping constituent rhEPO in blood units.

Agriculture is a significant and vivacious domain in the fiscal evolution of the globe. With current population, climatic conditions and resources, agriculture turns out to be a challenging task to fulfill the requirements of the future population. Intelligent Precision agriculture also known as intelligent smart farming has emerged as an innovative tool to address current challenges in automated agricultural sustainability. This mechanism that drives this cutting edge technology, that is the machine learning (ML) giving the machine ability to learn without being explicitly programmed reinforced with rewards. AI and ML together with IoT (Internet of Things) enabled farm machineries are key components of the future agriculture revolution ahead. In this work, a systematic Gaussian Quadrature numerical analysis of ML applications in the field of agriculture is done. Fixing the right real-time problems followed by solving it for agricultural augmentation or amplification thereby leading to global best.

Aim: To study the spatial and temporal variability of shallow groundwater recharge using the Soil-Water Assessment Tool (SWAT) model on a watershed scale. Place of Study: Thuthapuzha subbasin. Methodology: Groundwater recharge is a significant factor in groundwater simulations, which may aid in realistic groundwater resource management and decision-making. The study assessed the spatial and temporal variability of shallow groundwater recharge in the Thuthapuzha subbasin using a semi-distributed hydrological model called SWAT. Land use map, soil map, topographic data (Digital Elevation Model [DEM]), and basic meteorological data are the general input data necessary to run the SWAT model. Results: Results from the study show that the average annual groundwater recharge in the Thuthapuzha subbasin estimated using the SWAT model is 201.26 mm/year (from 1992 to 2019). According to the long-term water balance of the whole watershed, runoff accounts for 60% of the average annual precipitation. Evapotranspiration accounts for 27% of the average yearly precipitation in the research region. Meanwhile, groundwater recharge is barely 8% of the average annual rainfall. The topography and land use in the study region were found to influence the spatial variability of groundwater recharge. The groundwater recharge also showed monthly and annual fluctuations. While the major rainy season (South-West monsoon) lasts from June to September, July to September period shows the highest recharge. The comparison of recharge obtained from the SWAT model with the recharge estimated using the rainfall infiltration factor (RIF) method shows a good correlation (R2 = 0.68). Conclusion: SWAT modeling is a feasible option over field-scale approaches for assessing groundwater recharge and its response to various influences on a catchment scale.

Aim: This study was aimed to determine the effect of intradialytic aerobic exercise on blood urea in hemodialysis (HD) patients. Introduction: HD is one way to treat advanced kidney failure and can help to carry on an active life despite failing kidneys. Intradialytic exercise (IDE) is the exercise training performed during the HD session to increase the patient’s strength and endurance. Materials and Methods: A quasi-experimental study was carried out among 60 adult patients on HD (30 each in control and experimental groups), selected by consecutive sampling technique, at selected tertiary care centers in Chennai. The baseline characteristics and pretest blood urea were collected from both the groups and the intradialytic aerobic exercise was administered for 8 weeks (3 times/week) among the experimental group of patients. Then Post-test I and II blood urea were assessed in both the groups at end of 4th and 8th week. The data were analyzed in SPSS 22. Results: IDE showed a significant effect in reducing blood urea (mg/dl) among experimental group of participants than control group in post-test I (76.73 ± 17.58, 93.20 ±19.24) and in post-test II (62.23 ± 13.28, 90.20 ±19.60) at P = 0.001. There was no significance difference in baseline data/pre-test between experimental and control group pre-test (103.53 ± 30.03, 103.70 ± 30.46). Conclusion: This study reveals that intra dialytic aerobic exercises is beneficial in terms of reducing blood urea, as a supplementary therapy for HD patients.

The semantic and XML in document classification are used to develop XML data based on tree-based document classification method. The document classification plays the main role in the information management and its retrieval of data, which is a learning problem. In a development context, document classification has a major role in many applications, especially in classifying, organizing, searching and representing concisely large information volumes. A swarm-optimized tree-based association rule approach is presented for the classification of semi-structured data with the use of soft computing. To improve document classification, a tree pruning technique to prune weak and infrequent rules and a binary particle swarm optimization (BPSO) method to optimize tree construction are proposed. An optimized tree-based association rule was proposed to improve XML documents classification based on BPSO, and tree pruning technique to prune weak/infrequent rules is presented. The method was evaluated by Reuters dataset. The Reuters dataset is applied for this method. Results show that the new method performs well for precision and recall compared with current methods.

The basic notions of CG-lower and CG-upper approximation in cordial topological space are introduced, which are the core concept of this paper and some of it's properties are studied. Furthermore, we have investigated some results, examples and counter examples are provided by using graphs.

There are subsequently several image registration methods and they ensure excessive impact in the medical world. It is applied in the data examining in medicine, investigating in remote sensing, satellite imaging, etc., In this paper the algorithm is put forward registers’ images of the identical modalities MRI T2 to MRI T2. There are some disadvantages of wavelet transform that have to be operated with the contourlet transform, but then that is now subsequently undertaking specific preprocessing after employing the contourlet transform harnessing the image enhancement technique using their low-subband that was got by the Laplacian pyramid as a part of decomposition and then reconstructing the image by using the reverse contourlet transform and then registering the reconstructed image with the targeted image by utilizing the geometrical transformations. The investigational outcome demonstrates that the Evolve Contourlet Transform imparts advantageous conclusions than the wavelet and also the traditional contourlet transform.

Numerous medical means are mandatory with accuracy and totality of data about a patient for a doctor for healthcare predictive analytics and so as to treat and take care of their patients’ remedy. Medical image registration practices bestow more affluent diagnosis and medication information to the doctors in stipulating an all-embracing allusion resource for the beneficiaries entangled in image registration as an optimization crisis. In this research work, optimization of theGenetic Algorithm based image registration technique using Fast Walsh Hadamard Transform was implemented and their evaluation of results proved to be good.

The learning cycle dependent on which machines are prepared, or educated to machines, utilizing all around organized information. That is, machines are prepared with information with the right responses to the inquiry, and these are made to be repeated in the memory of such machines. In this way the machine takes in a couple of subtleties from the preparation information previously gave and afterward applies that information to the test information. One area is utilized to prepare the model and another to test or assess the model, haphazardly isolating the arrangement of information. Direct backslide is a technique for showing the association between a scalar ward variable y and in any event one intelligent factor (or free factor) implied X. The example of one enlightening variable is called fundamental straight backslide.

Predictive analysis plays a major role in healthcare industry where forecasting the disease will reduce the risk that happen to patients. Statistics show that cardiovascular diseases have increased the mortality rate in India. Machine learning which is used in developing a predictive model for various domains is nowadays applied in the field of medical diagnostics. Machine learning is playing an integral role in predicting the presence or absence of heart diseases. Such predictions, if done well in advance, can help the doctors to carry out the treatment for the patients and mitigate their health risk. Biological samples such as blood or tissues are collected from the human body to predict cardiovascular diseases. The proposed work is focused on developing various machine learning predictive models using support vector machine, decision tree, neural network and K-nearest neighbour for prediction of heart disease. For this work Cleveland heart disease dataset is used which consists of 14 attributes and 294 records. A comparative analysis on the prediction models were carried out. From the results, it was found that support vector machine, decision tree and KNN (k = 15) classifiers yield better accuracy to predict heart disease than the other models.

With the tremendous development of internet, we can share any media from anywhere in the world. This paved the way for data sharing illegally - pirated versions of data shared among persons. In our proposed system, the original data use case taken - Song will be embedded with a hash value SHA 256 and then deployed to Inter Planetary File System IPFS and shared through Ethereum Blockchain, enabling deployed data unaltered. The IPFS returns a 46 bit length hash for each of the file being upload. Usage of Ethereum blockchain ensures each and every transaction cryptographically hashed and logged. Also, the data deployed on IPFS sharable but with hidden hash identity for each of it, which is not known by the person who gets that data. The owner of the original data thus shares the data and will be having the log maintaining hash identity for each of the person to whom the data owner shares. The IPFS storage enables Peer to Peer P2P data transfer through decentralized network. The person will be given the IPFS hash to download the data. When the data if pirated is known to the owner, he computes the hash value from the pirated version and identifies the person pirated that data and avoids sharing data to that person next time Arun Kumar G | Hariharan A | Ms D Sasikala "Avoiding Data Piracy in Artworks using Blockchain" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd11244.pdf

Proflavine is a small molecule that intercalates into DNA and, thereby, acts as an anticancer agent. Intercalation of proflavine is shown to be a two-step process in which the first step is believed to be the formation of a pre-intercalative outside bound state. Experimental studies so far have been unable to capture the nature of the outside bound state. However, the sub-millisecond timescale observed in fluorescence kinetic experiments is often attributed to the binding of proflavine outside of DNA. Here, we have performed molecular dynamics simulations with multiple proflavine molecules to study the structure and dynamics of the formation of the outside bound state of DNA at different ion concentrations. We observed that the timescale of the outside bound state formation is, at least, five orders of magnitude faster (in nanoseconds) than the experimentally reported timescale (sub-milliseconds) attributed to binding outside DNA. Moreover, we also observed the stacked arrangement of proflavine all around DNA, which is different from the experimentally predicted stacking arrangement perpendicular to the helical axis of DNA in the close vicinity of the phosphate groups. This study, therefore, provides insight into the molecular structure and dynamics of the pre-intercalative outside bound state and will help in understanding the overall intercalation mechanism.