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In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

The land use exercises at last influence the land cover briefly as well as spatially. The primary consideration liable for the adjustment in land cover is to satisfy the developing requests of expanding populace through horticultural increase for nourishment and clearing of typical land covers like the forests and sparsely inhabited regions for settlement and business activities. The adjustment in land cover additionally upsets the other characteristic parts like soil ripeness, soil disintegration, environment, biodiversity, air quality and water system of the upset area. Remote sensing and GIS have been deployed to examine the changes in land use and land cover of the zone at spatial and transient scales. In the present study, the NDVI-based grouping explored a significant change in land use- land cover between 2009 and 2016. A significant change has been found in the forest cover area where around (3.34%) of the forest was occupied between 2009 and 2016. A comparison between the land use/land cover and landslide hazard zones was carried out which indicated the excessive anthropogenic activities (of construction and mechanized agriculture) as identified by built-up land and crop-land by NDVI studies need to be monitored and planned to maintain the safety, stability and popularity of this precious and geo-climatically strategic region.

Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.

In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, i.e., text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition (DWT-DCT-SVD) is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of 0.99, and an embedded watermark resilient to various attacks in the watermarked image.

The world “probiotics was coined by Parker (1974), and defined as “Organisms and substances that give to intestinal microbial balance. Fuller (1989) revised the definition as “live microbial feed supplement which beneficially affects host animal by improving its intestinal microbial balance”. Probiotics are often termed as “friendly”, “beneficial”, “good” or “helpful” bacteria, because they help keep the gut healthy. More recently, the probiotics are defined as “live microorganisms” that when administered in adequate amounts confer a health benefit on the host (FAO / WHO, 2001).

The present study : “Studies on the effect of ginger, garlic and fenugreek powder supplementation diets on the growth and survival of pacific white shrimp, Litopenaeus vannamei’’ was conducted in the Wet Laboratory of the Department of Aquaculture, College of Fishery Science, Sri Venkateswara Veterinary University, Muthukur. L.vannamei shrimp was fed with three different herbal powder of ginger , garlic and fenugreek of varying levels of 1%, 2.5%

The Casson viscous gas fluid flow with mass transpiration and radiation is demonstrated in this paper. A similarity transformation is utilized to interpret the representing construction of nonlinear PDEs by nonlinear ODEs. Then, using an exact analysis of the momentum equation, we constructed the dual solutions to the flow problem. This solution domain aids in the solution of the energy equation under the incomplete gamma function condition. The effect of radiation can also be seen in the heat equation. Physical parameters of interest, such as Brinkman number, porous media, thermal radiation number, induced slip, suction/injection and shear stress, are examined and graphically depicted. The gas slip velocity is used to simulate the slip flow model of the overall mass transfer on the moving sheet. Models of first- and second-order slip are introduced to induce the total mass transfer on the moving sheet. Additionally, the suction that causes the slip velocity rather than surface movement is investigated. The neighboring gases are forced to flow in the opposite direction of sheet movement by the mass suction-induced slip. As a result, the slip-induced suction and sheet movement cause the solution space to increase. The closed-form exact solutions are achieved for both the stretching and shrinking sheet cases. For stretched sheet instances, there is never a uniform solution. However, depending on the values of the Casson and wall mass transfer parameters, the solution in the case of the shrinking sheet may or may not exist, and if it does, it may or may not be unique or may have a dual nature. The research also demonstrates that stronger mass suction is required for a consistent flow of Casson fluid. The impacts of inverse Darcy number, induced slip parameter, Casson fluid and suction/injection on the flow and heat transfer properties of the fluid are examined under the influence of radiation, and the solution of each profile is shown in the form of figures along with the outcomes of the interface velocity and heat transfer rate at the surface.

The hardened properties of concrete are one of the most important parameters when considering the sustainability criteria of buildings. Numerous previous studies have established how the Artificial Neural Network is used as an effective statistical data modelling tool. In this study, the prediction of Elastic modulus and Compressive Strength of steel fibre reinforced concrete have been illustrated with a feed-forward backpropagation neural network structure. 158 datasets are used for modelling of elastic modulus and 140 datasets is used for modelling of Compressive Strength. The developed ANN models were able to predict the data within the range of input parameters considered and were having regression coefficients values of 0.96 and 0.97 respectively. A comparison of actual and predicted values has been performed and the results indicated that the ANN performed better in terms of prediction with minimum deviation from original data.

A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated.

Hospital specific monitoring studies aims to determine the prevalence of the type of microorganisms that causes urinary infections and to assess the antimicrobial sensitivity pattern. A hospital based short term prospective cross-sectional study was conducted at Sagar Hospital, Bengaluru from August to October 2020. Antimicrobial susceptibility in MIC test was performed for the isolated pathogens by using automated VITEK compact method and stratification was done according to ICU and NON-ICU patients with over 260 urine culture tests of the suspected in-patient for urinary tract infection. All antimicrobial susceptibility data was analyzed using WHONET software 5.6. Sixty-six urine culture tests showed culture positive, 41 from non-ICU and 23 from ICU. Gram-negative bacilli E.Coli (61% in ICU, 52% IN non-ICU) was the most prevalent bacterium, followed by Klebsiella pneumoniae ss. pneumoniae (9% in ICU, 20% IN non-ICU), and Candida albicans 9% prevalence was seen only in ICU isolates. The study shows that the rate of resistance towards ampicillin (100%) was high among gram negative isolates and penicillin (100%) among gram positive isolates. The susceptibility was favorable towards polymixinB, colistin in gram negative organisms and nitrofurantoin, vancomycin in gram positive organisms. The present study revealed that E.Coli is the predominant bacterial pathogen. It also demonstrates there is an increasing resistance to ampicillin (88.2%) and ESBL (90%) among UTI pathogens. While, polymixin B (100%) and colistin was found most effective among gram negative and nitrofurantoin was most effective among gram positive organisms. Keywords: Antibiotic, antimicrobial resistance, urinary tract infection

Plant derived products play a vital role in preventing and treating various disease in humans. Tephrosia tinctoria is a plant belonging to the family Leguminosae, found to have antidiabetic and antioxidant activities. The study was aimed to investigate the anti-diabetic and anti-oxidant activity of whole plant of Tephrosia tinctoria in diabetic rats. Alloxan induced model was used to induce Diabetes. The chloroform and ethanolic extracts of Tephrosia tinctoria (CETT and EETT) at the dose of 250 and 500mg/kg b.w were administered orally at single dose per day to diabetic rats. Glipizide 5mg/kg b.w was used as standard drug. The general body weight, insulin level, blood glucose, serum lipid profile, superoxide dismutase, and lipid peroxidation assays were the parameters evaluated in diabetic rats. EETT have better anti-diabetic and anti-oxidant activity than CETT. The protective effects were even confirmed by histopathological studies. These observations show that both the extracts were effective in possessing the significant antidiabetic and antioxidant properties in alloxan induced diabetes.

Social networking platforms like, Twitter, Face book etc., have now emerged as a major forum for the application of Recommended Systems (RSs). Of course these famous sites are taken as the main source of people-related knowledge and thus to be a great choice for exploiting modern and creative approaches to the recommendation, backing the old methods, in order to improve accuracy It was thought that helping users cope with the issue of data overload was the original role of information retrieval systems or search engines, but what separates recommended systems from the existing search engines is the requirements of personalized useful and interesting. The "intelligence" aspect is what suggests more interesting and useful. Intelligence is one of the main routes of personalization to know the interests of the user, anticipate the unknown favorites of the user, and eventually provide suggestions by matching the question and the content beyond a basic search. This article provides simple approaches to Recommendation Systems, provides recommendation for similar items based on the correlation and classification methods of machine learning to build a collaborative filtering system by making use of Logistic Regression model.

Scoliosis is one of the most common diseases that is thrown under the radar. The lateral curvature and rotation seen in the vertebrae of the spinal column is classified in mostly 2 types: C-Curve and S-Curve. People need to be aware of the early signs and symptoms so that it is diagnosed in the proper time and manner. Scoliosis is mostly diagnosed and identified by taking X-ray medical images of the spine and based on the sideways curvature image modality. In traditional scoliosis diagnosis detection, the treatment is based on spinal assessment which a manual study consisting of major limitations mainly is being very tedious, time-consuming, and cost effective. Till date, without the assistance of technology, scoliosis diagnosis has been a critical task in the beginning because it all depended on the patient history records or even the captured x-ray images of the patient. Hence to ease up the process and provide better treatment experience, our research work is focused on categorizing the type of scoliosis in an effective, accurate and reliable way. This is obtained by analyzing and processing the input X-ray images obtained from the datasets and even the patients suffering from scoliosis. To overcome the aforementioned limitations, we developed a point-based automated method at different regions of the spinal cord resulting in accurate results using the Convolutional Neural Network (CNN) algorithm and further compared it to Support Vector Machines (SVM) for better understanding.

Marine fisheries and biodiversity in the Gulf of Mannar, Tamil Nadu, were severely impacted by the blooms of dinoflagellate Noctiluca scintillans, on 10th October 2021. The bloom affected the marine biodiversity in a 15 km2 area between Keelarai to Mudal in the Gulf of Mannar, Tamil Nadu, India. Post bloom observations showed mass mortality of a variety of finfish, ornamental fish, bivalves, shellfish, sea anemones, sea slugs, sea cucumbers, sea horses, polychaete worms, and seaweed that washed ashore. Two damselfish species Dascyllus reticulatus and D. trimaculatus were deleteriously affected. The intense N. scintillans bloom killed 0.83 tons of damselfish, equivalent to approximately 9 million fish, and 1.67 tons of commercially important fish valued at US$ 11.5 M.

With the development in automobiles, traffic light regulation has become a serious concern, and it is also necessary to eliminate time delays while travelling in traffic due to long delays at traffic signals. So, after thinking about this issue, we've decided on a Smart Traffic Light Signaling System with IR Sensors. This article shows ways to cut down on traffic delays in high-traffic locations. We are using infrared sensors in this system to determine the traffic density at traffic signals. We will install IR sensors on each road in the traffic junction for this purpose. These infrared sensors will determine the density of traffic on that road. Sensors are connected to a microcontroller. The microprocessor will operate the lights based on the density of traffic detected by IR sensors. The traffic density will be detected by IR sensors, and the microcontroller will operate the traffic signals accordingly based on the density of traffic. Traffic messages will be printed on the LCD under this system

Organisational competitive procurement environment positively impacts prices while supply side factors also determine participation in bidding. With strong competition costs come down. Instances of low competition in equipment purchase in the apex tertiary care hospital led to this study aimed at determining procurement practices, competition in procurement and its determinants. Review of tender records for three financial years (2011-2014) was conducted to ascertain procurement practices and resulting competition. Sellers' perspective on factors determining participation was captured through a questionnaire. Although four bids per tender were received only two qualified technically emphasising need for broad based specifications. Both sellers and peer staff also feel that specifications are not broad based. Regression analysis showed that one unit increase in bid led to six per cent price reduction. Increase in number of equipment led to an increase in the number of bids received but no price decrease. Pre-bid meetings have no effect on the price offered. Quality of tender document and feasible demonstration are modifiable bidding factors. Procuring agencies need to be transparent to reflect an organisationally supported competitive environment. The future research directions are interventional studies on specifications, pre-bid meetings and increasing transparency.

In this experiment, the investigation on performance and emission characteristics of Jack fruit peel biodiesel is done in single cylinder, constant speed, water cooling system Variable Compression Ratio engine using single hole nozzle at various loads. Jack fruit peel Biodiesel is prepared by transesterification process. Analysis is done with three combination of Jack fruit bio-oil with diesel as 20:80, 40:60 and 60:40. From the study it is found that the emission characteristics of Carbon monoxide, Hydrocarbon and Oxygen in biodiesel are less. On the other hand Nitrogen oxide and Carbon dioxide are less for diesel. The specific fuel consumption (SFC) and Break thermal efficiency (BTE) of these blends are high for biodiesel.

Massive reliance on practical systems has resulted in several security concerns. The ability to identify anomalies is a critical safety feature enabled by anomaly diagnostic techniques. The construction of a data system faces a significant issue in cyber security. Because of the exploitation of valuable data, cybersecurity impacts the privacy of such data. Attack incidents must be examined using an appropriate analytics approach in elevating the safety level. Design of advanced analytical, conceptual model creation gives practical guidance and prioritizes threats/attacks across the network system. There is now substantial effectiveness in attack categorization, and evaluation through Convolution Neural Network (CNN) based classifiers. In light of the drawbacks of previous approaches, this research proposes an approach relying on the Deep Learning (DL) strategies for cyberattacks detection and categorization in the context of cyberspace incidents. Likewise, this article presents an XGBoost Regression Classifier (XRC) using Inception V4 to address those restrictions. XGBoost refers to Extreme Gradient Boosting, a decentralized gradient-boosted decision tree (GBDT) supervised learning framework that is robust and can be used in a decentralized context. XGBoost is a well-known machine learning technique because of its ability to produce outstanding accuracy. The concepts of both XGBoost and Regression classifiers are integrated and represented as a suggested hybridized classifier, which is implemented in Inception V4 to further train and test the model. The proposed XRC categorizes and forecasts several common types of network cyberattacks that includes Distributed Denial of Service (DDoS), Phishing, Cross-site Scripting (CS), Internet of Things (IoT). The sigmoidal function is used as a supportive activator to the hybridized classifier to lower the erroneous ratio and increase the effectiveness. Research shows that training and testing errors were substantially decreased when using XRC. In 9 out of 13 instances, over 97% of threats are detected by the XRC, and over 75% of threats are detected in its most challenging datasets.

The presence of Residual monomer content in processed dentures is not desirable. Different methods are used for reducing the residual monomer content after processing. In the present study the effects of two post cure treatments (water immersion and additional curing cycle) on the residual monomer content of two commonly used denture base resins (Acralyn H and Trevalon Hi) were evaluated by using gas chromatography. Study concluded that both water immersion for one day at room temperature and additional curing cycle reduced the residual monomer content in both brands which was initially high after processing.

Gesture recognition is a type of perceptual computing user interface that allows computers to capture and interpret human gestures as commands. Gestures are generally the movement of the hands which is a form of non-verbal communication. We use gestures as input to control devices or applications or websites. By using the gestures, we directly open or manipulate the website for the users. When the user uses the gesture, the system captures the gesture and compares it with the stored gesture data. If the gesture matches the data, then the required website will be manipulated. These gestures are widely helpful for the people who are blind and unable to type. The main objective of the project is to deliver user friendly access to the web contents. The vision based technology of hand gesture recognition is an important part of human-computer interaction (HCI), thus stepping into the path of future and our project is one such kind of stepping stone. Our project really concerns the people who are not able to search contents on the web through text search. We provide an improvisable solution where it paves a way for research and development.

Today’s business environment, survival and making profit in market are the prime requirement for any enterprise due to competitive environment. Innovation and staying updated are commonly identified two key parameters for achieving success and profit in business. Considerably supply chain management is also accountable for profit. As a measure to maximize the profit, supply chain process is to be streamlined and optimized. Appropriate grouping of various suppliers for the benefit of shipment cost reduction is proposed. Data relating to appropriate attributes of supplier logistics are collected. A methodology is proposed to optimize the supplier logistics using clustering algorithm. In the proposed methodology data preprocessing, clustering and validation process have been carried out. The Z-score normalization is used to normalize the data, which converts the data to uniform scales for improving the clustering performance. By employing Hierarchical and K-means clustering algorithms the supplier logistics are grouped and performance of each method is evaluated and presented. The supplier logistics data from different country is experimented. Outcome of this work can help the buyers to select the cost effective supplier for their business requirements.

Flexible silicone tubes are nowadays broadly employing in medical applications such as drugs or blood-conveying tubes, various infusions as well as medical pumps, and ventilators tubes. Due to better resistance to corrosion and erosion, the subsequent diminishing of wall thickness and change in stiffness of silicone tubes are negligibly small. Hence, these tubes provide trouble-free operations for a prolonged period. Commonly, the fundamental frequency of fluid conveying tubes is influenced by various flow parameters. Recognizing the parameters affecting the fundamental frequency and estimating the extent of the impact of these parameters on the natural frequency are extremely important. This information is vital in the design of precision flow measuring instruments such as Coriolis flowmeter (CFM), which uses vibration techniques for flow measurements. Owing to higher accuracy and the direct fluid flow rate measurements, CFMs are universally accepted in industries for flow measuring applications. When the flexible silicone tube is excited in its sagged condition, there exists a difference in natural frequency in horizontal and vertical planes. Applying pre-stretch decreases the difference in natural frequency, which reduces the sagging and facilitates the tube to regain its cylindrical shape. This paper shows the experimental research on the influence of sagging of the silicone tube conveying fluid, the feasible methods to overcome these effects, and an effective means to capture the deformed shape of a flexible tube. Numerical analysis is carried out in ANSYS using the deformed shape acquired from the laser scanning technique, and the result shows excellent agreement with the experimental values. The damping ratio of the tube is identified by experimenting with varying flow velocities and pre-stretches. This study reveals that it is essential to eliminate the effect of sagging and the subsequent impact of frequency difference in flexible tubes when the principle of Coriolis flowmeter employs for measuring flow rates through flexible tubes.

The Petroleum resources are depleting rapidly. Biodiesel is one of the most favourable alternatives for Fossil Fuels. Vegetable oils are coming from agricultural sources. Hence CO2 emission in the environment can be reduced when compared to petroleum products. In this investigation Jujube Methyl Ester is prepared by transesterification method using Potassium hydroxide (KOH) as catalyst. It is used in Four Stroke, single cylinder variable comparison engine (VCR). The rated speed of the engine is 1500 rpm at different loads. The engine performance was analyzed for different nozzle and blends. Nitric oxide (NOx) and Particular matter (PM) are slightly increased. Carbon dioxide (CO2) is apparently increases with load whereas HC is found to be decreasing with load. Carbon monoxide (CO) emission is high for this.

This paper discusses about the analysis of 49″ Earth Movers lock ring. This lock ring is made up of medium carbon steel (S43C) where the carbon present in the percentage of 0.4–0.46. Lock rings is the final piece of the assembly of Wheel and it holds the all components of the Wheel such as Flanges, Bead seat Ring, Rim base & Gutter. In order to work properly, the lock ring requires proper mating parts, so you must always ensure that the correct gutter section and bead seat band are used. In the 49″ inch EM Lock ring Stiffness is quite less due to diameter is large. While more than 4–5 times the lock ring is dissemble and assemble stiffness of the ring is reduced as result as air is being leak from the tire. To prevent this issue, it is planned to do the heat treatment analysis for the ring. By doing Hardening process the mechanical properties of the material is altered. The metal is heated up to 200 °C for 150 min then the metal is exposed to atmosphere for cooling process. After the Hardening process materials properties are checked by using Ansys Software. As the result of material stiffness is improved & have an ability to withstand for 10–15 times of assemble & dissemble.

As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this work. The data mining preprocessing using Synthetic Minority Oversampling Technique (SMOTE) has been employed to cope with the imbalance problem of the class as well as noise. The proposed work has two steps. SMOTE is used in the initial phase to reduce the impact of data imbalance and the second phase is classifying data using Naive Bayes (NB), decision tree (DT) algorithms, and their ensembles. The experimental results demonstrate that the AdaBoost-Random Forest classifier provides 95.47% accuracy in the early detection of heart disease.