Reviewerly/exHarmony huggingface dataset:
The Detection of the Upper Boundary of Breasts Using 4D Scanning Technology

Defining the boundary of female breasts is an unavoidable and very essential step before estimating the volume or surface area of the breast.Anthropometric measurements such as these are critical to the understanding of breast shape and the development of bra products.However, a non-contact method that accurately detects the boundary between breasts and chest wall, for breasts evaluated in the upright body position, has not been reported before.In this study, with the help of 4D body scanning and taking advantage of the time delay in the vertical displacement between the breasts and the chest wall during physical activity, we 4D-scanned 26 female participants using the Temporal 3dMD system and proposed a method to visualize the amount of variability for the relative displacement in the vertical direction, to facilitate the definition of the upper boundary of breasts.By viewing each breast as a cone or hemisphere, and processing the scan through vertical slicing, we were able to make all the scans having the same number of points (7200= 180×40.There were 40 vertical slices in total, and on each slice, there was one point located at every other degree from -180° to 180°) sorted in the exact same order.After proper alignment, we calculated the z-coordinate difference between a scan captured while the participant was running (there were nine dynamic scans that formed a gait cycle) and the corresponding scan of the same participant captured in static standing (the static scan).Then we were able to calculate the standard deviation (SD) value of the z-coordinate differences (i.e. the relative vertical displacements) across the nine dynamic scans.Heat maps were then created with the SD values mapped onto the 3D surface of the static scan and presented by gradient colors.This study is one of the first comprehensive studies that investigate the vertical displacement and the shape deformation of breasts during running using 4D scanning technology.Ultimately, the method and results can increase the understanding of breast kinematics, and benefit the product design of bras, especially for sports bras.
Soft Computing Technique Based on Missing Value Treatment

Missing value treatment is an actual yet challenging issue confronted in data mining. In existing work missing value treatment is a procedure that replaces the missing values in a dataset by some conceivable values. The conceivable values are generally generated from the dataset using a statistical evaluation.Thesetypesofresultdonotgiveaccurateoutcomes.In this paper, soft computing is used in the random forest approach using for missing value treatment that isdevised and implemented on the different types of social media. Using random forest approach results are improved form existingtechnique.
Continuous Integration and Continuous Delivery Platform Development of Software Engineering and Software Project Management in Higher Education

We present a report of development phase of a platform which aims to enhance the efficiency of software project management in higher education. The platform accommodates a strategy known as Continuous Integration and Continuous Delivery (CI/CD). The phase consists of several stages, followed by testing of the system and its deployment. For starters, the CI/CD platform will be deployed for software projects of students in the Faculty of Information Technology, Universitas Kristen Maranatha. The goal of this paper is to show a design of an effective platform for continuous integration and continuous delivery pipeline to accommodate source code compilation, code analysis, code execution, until its deployment, all in a fully automated fashion.
Prediction of disease using fuzzy random forest

The learning approach is basically used for classification of different data into clusters. Basically, semi supervised learning has been used worldwide to classify the labelled as well as unlabeled data. The dataset sometimes may be in mixed features that may consist of both numeric and categorical type of data. In these two types, data may differ in their characteristics. Due to the differences in their characteristics, in order to group these types of mixed data, it is better to use the ensemble clustering method which uses split and merge approach to solve this problem. This research work carried out the original mixed dataset and is categorised into numeric dataset and categorical dataset and clustered using both traditional clustering algorithms and fuzzy clustering algorithms using random subspace approach called as fuzzy random forest (FRF). The resultant clusters are combined using ensemble clustering methods and evaluated by both f-measure and entropy measure. It is found that splitting is more beneficial and applying fuzzy clustering algorithms provides better results than traditional clustering algorithms. The system was tested on Hadoop multi node cluster environment as well as traditional environment. The hybrid genetic algorithm is used for optimisation.
Robust Watermarking Scheme in YCbCr Color Space using SVD and DKT-DCT Hybrid Wavelet Transforms

Digital image watermarking is a significant one for copyright protection. In this work, a robust watermarking method that can be utilized to ensure copyright is proposed. The proposed technique improves the power of the watermark with the end goal that it very well may be utilized to secure the intellectual property considerably after genuine assaults on the watermark picture. A YCbCr model can be utilized as the ideal color model, the Y part being the ideal color channel here. Frequency domain methods give more robustness. Changes like DCT, Wavelet changes and Singular Values Decomposition (SVD) are some all around investigated transform domain procedures in watermarking. DKT-DCT hybrid wavelet transform is investigated by applying on have and utilized for embedding singular values of watermark. The proposed plan perceptually enhanced one and more strong in opposition to attacks and rotation invariant. The test outcomes show the perceptually enhanced condition as well as the efficiency of the proposed method when compared with traditional methodologies.
Examination and Forecasting of Drug Consumption Based on Recurrent Deep Learning

The provision of pharmaceutical drugs in quantities appropriate to consumption is an important point in the pharmaceutical industry and storage of medicines, as the production of large quantities of unnecessary drugs leads to a longer storage of drugs. Meanwhile most medicines have a short shelf life. When the amount of production is less than required, this affects the satisfaction of the customer and the marketing of the drug. Time series analysis is the appropriate solution to this problem. Deep learning has been adapted for the purpose of time series analysis and a prediction of the required quantities drugs. A recurrent neural network with Long-Short Term Memory LSTM has been used by deep learning. The proposed methodology is based on the seasonal number of prescription required quantities with the number of quarters as indicators. The aim of the research is to forecast the drugs amount needed for one year. The proposed method is assessed using two types of evaluation. The first one is based on MSE and the visualization of the actual data and forecasted data. The proposed method has reached a low value of MSE and the visualization graph is semi-identical, whereas the second evaluation method compares the result of the proposed method with traditional forecasting method. Multiple linear regression is a traditional prediction method used with the data set, whose results are relatively good and promising compared to the results of the traditional method.
A Conception for Identifying Trust Service Providers in Collaboration Cloud Computing

cloud computing model has proven to be effective and popular in offering the benefits such as virtually limitless storage volume, availability and scalability. Hence the cloud environment is managed by varied trust management models. Choosing the most suitable trust management model by the user is often a difficult task. Since there is multiple cloud services offering different level of performances with same functionalities it becomes hard for the user to detect and select the apt cloud service. Csps tend to offer cloud related services to the cloud users keeping sla in consideration. Depending on sort of csp administration offered the payment by the consumer end, each transaction is being evidently distinguished. Regrettably, since qos (quality of service) is not achieved, sla is not looked upon seriously. In various situations and recommendations, administration presence doesn’t fulfill the standards and reliability is compromised. The current research paper deals with the difficulty in choosing qualitative csp (cloud service provider). The proposal is of a methodology to reveal the csps trustworthiness pertaining to a cloud scenario. The various levels listed in this mechanism or methodology being: building up of cloud cluster environment, broker creation, machine agent, feedback provider and trust validation. The mentioned system of trust management gains fewer machine agents, feedback is given by external resources, highly effective in trust computing, cpu’s of machine won’t be left idle or sluggish. It can be stated that the proposed mechanism yield to be effective and feasible as validated by the performance analysis and experimental outcomes. The outcome demonstrates the effectiveness of the approach concerning services matching. With effective use of trust model specific service can be chosen by the cloud user.
Reconstructing Cardiac Shape via Constrained Voronoi Diagram and Cyclic Dynamic Time Wrapping from CMR

Reconstructing cardiac shapes from CMR short axis parallel contours is a prerequisite task for downstreaming analysis and diagnosis.This task, however, is not trivial when the inflow and outflow tract must be taken into account.These tracts are a difficulty for reconstructing whole cardiac shapes from CMR.We aimed to develop a method to reconstruct either left cardiac structure or right cardiac structure with minimal user assistance in a unified geometric framework.Ventricular, atrial, and arteries contours were delineated on short axis images.Cyclic dynamic time wrapping (DTW) method were utilized to triangulate two parallel contours on neighboring imaging slices.Reconstructing three contours on inflow and outflow tracts were divided into two parts.The outer part was reconstructed from the lower contour and the convex hull of two upper contours.The interior part was parobolic surfaces fit to points not existing in the convex hull and the Voronoi cell centroids.All cases were automatically reconstructed successfully with average computational time less than one minute.Reconstructed shape have anatomically reasonable structures.
Image Forgery Detection with SIFT and RANSAC

in this paper, simulations were performed using SIFT and RANSAC to highlight the forged regions in the doctored image. SIFT algorithm is modified to consider unit vectors as the features of the blocks. Blocks with similar unit vectors were grouped into cluster. Mean values of the clusters were compared to determine the similarity between clusters. Once the clusters were formed, the image was subjected to RANSAC algorithm to determine the geometric transformation and to highlight the forged region in the doctored image. Two simulations were performed to test the performance of the proposed method. First, doctored image with only scaling and next, image with both scaling and rotation were tested. The simulation results are presented in detail.
Authentic cloud-biometric signature verification system for healthcare data management

Nowadays, cloud computing is the fastest growing technology in the world in various fields such as engineering, games, healthcare and agriculture. Data maintenance is an important aspect in the healthcare management for diagnosing diseases and or further treatments. This study presents authentic cloud-biometric signature verification for healthcare management. Data security is the major issues in healthcare management. It is led to tax, bank, insurance and medical fraudulence. Therefore, the retrieval of medical data onto secured access is essential to improve the protection of healthcare services. Authentic cloud-biometric signature verification system for healthcare data management has been designed using neural network for data protection. The accuracy of data retrieval from cloud healthcare is measured using neural network. The neural network acquires biometric signature through biometric sensor processed with quality checker for effective authentication. This network also supports in terms of statistical learning of the clinical datasets. After various experiments, it is concluded that the proposed method provides faster results with higher sensitivity and specificity rate of 0.98 and 0.95, respectively. In comparison with other state of art methods, it is found that the cloud healthcare data security system attains better performance than the existing systems.
Towards a formal specification for an AADL behavioural subset using the LNT language

The analysis of real-time systems designed by architectural languages such as architecture analysis and design language (AADL) is a challenging research topic. In such a context, formal methods become an advocated practice in software engineering for rigorous analysis. Yet, they are applied on specific formalisms to be analysed on dedicated tools. This paper studies the formal verification of real-time systems modelled with the AADL language and its behaviour annex. We define a formal semantics of an AADL behavioural subset using the LNT language. This work is illustrated with a robot case study.
Improved Capsule Network for Gaze Estimation in Wireless Sensor Networks

In this study, aiming at the problem of gaze estimation in the wireless sensor network in the car, we use image-based method to estimate gaze based on the single camera sensor.We use the deep learning model and propose the improved model from three aspects based on the original capsule network.The first is to increase the convolution layer, the second is to increase the capsule layer, and the third is to widen the capsule layer in the network.Through many contrast experiments, it is proved that the appropriate use of the first or second improved method can achieve performance over other comparison models, and the prediction results of gaze estimation are almost no different from the real gaze direction.
Classification of Semantic Similarity Technique between Word Pairs using Word Net

The concept of relevancy is a most blazing topic in information regaining process. In the last few years there is a drastically increase the digital data so there is a need to increase the accuracy of information regaining process .Semantic Similarity measure the similarity between word-pair by using WordNet as ontology.We have analyzed the different category of semantic similarity algorithm to compute semantic closeness between word-pair and evaluate its value by using WordNet.We have compared various algorithms on Miller- Charles data set of 30 word-pair is used to rank them category wise.
Identifying the concept of Image and Captioning Using Deep Neural Networks

In recent years, Image captioning has become a challenging artificial intelligence problem.Many researchers have been interested in the field of AI and became an arduous and exciting task.Image captioning automatically generates the textual description consistent with the content observed in a picture, and it is the mixture of two methods, including computer vision and natural language processing.Computer vision is to understand the images' content and natural language processing to understand the image into words in the correct order.Recently, Deep learning methods are achieving better results on caption generation problems.They can define a single end-to-end model to predict a caption when a photograph is given, instead of requiring a pipeline of specifically designed models or sophisticated data preparation.By using deep learning techniques like CNN, RNN accurate descriptions can be predicted.Convolutional Neural Network (CNN) implicitly extract features from the image, and Recurrent Neural Network is used for sentence generation.The developed model was trained to capture the image concept and generate the textual description observed in an image.
Performance Analysis of Fractional Redundant Wavelet Transform for Watermarking Scheme

In this paper, an algorithm for digital image watermarking which utilizes the hybridization technique is presented. The hybrid technique is formulated by combining the Redundant Wavelet Transform (RDWT) with Fractional Fourier Transform (FrFT) and Singular Value Decomposition (SVD). In this technique, watermark information is embedded in the low frequency band of Redundant Wavelet Transform. To increase the robustness, FrFT is implemented on low frequency coefficients of RDWT. Experimental results have been demonstrated on the basis of Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), and Gradient Magnitude Similarity Deviation (GMSD). A comparable improvement is witnessed from the results in terms of qualitative and quantitative analysis. The experimental results prove to be robust against various image processing and geometrical attacks applied on the standard test images.
A Probe on Crime Data in Various Domains

The word 'crime' is not a word; there is a lot of pain and loss behind it. The word 'crime' is the most horrible word that scares human society. Even as this article is being read, somewhere in a corner of the world, a man is currently suffering as a result of a crime. Crime has changed its shape over time according to the dimension of men. The system of detection of crimes and punishment of criminals has reached considerable development. Efforts have been made to prevent crimes in the past, but no amicable solution has been reached. To date, there have been lots of strategies to prevent this. The intention of this research article is to analyze the research articles that have been used in various criminal cases/activities that have taken place at different locations, and to identify the criteria, concepts, tools, conclusions, credentials and shortcomings found in those articles.
Digitalization of Street Hawker and Vendor Services

Since the marketplace is far from the residential colonies, people prefer to buy goods from street hawkers and local vendors due to easy availability. However due to absence of any proper system, there are problems faced by the customers, hawkers as well as the general public. There are several problems that are caused by Street hawkers and vendors in terms of traffic jams and occupying public spaces such as footpaths, pavements, etc. Improper way of disposing food waste and garbage by hawkers is another issue. They cause congestion on the roads. However, the vendors and hawkers also face a lot of problems and are harassed by the customers and the city authorities such as The Municipal Corporation, Police, etc. which leads to exploitation and extortion. They also have a right to earn their livelihood in a respectable manner. In order to resolve this problem, we intend to develop an android application ‘Vendiz’ which will lead to a proper digital system and interaction between customers and the street hawkers and vendors and help the customers to navigate through the available items at different vendors via the application.
HIDROLOGICAL SIMULATION OF THE RIVERS’ FLOOD CONTROL IN SUBDISTRIC WONOKERTO IN PEKALONGAN REGENCY

ABSTRACT:Flooding problem still become serious problem in subdistrict Wonokerto of Pekalongan regency. There are six rivers that have the potential caused flooding in setelment area. In this research, the researcher wanted to do hydrological simulation at six rivers in Wonokerto. The hydrological simulation is done by calculating the inflow in the river in the form of flood discharge hydrograph design with nakayasu method, and evaluate the capacity of river catchment to flood discharge. Based the results research on six rivers, it is known that the river capacity is not able to accommodate the flood discharge when enters the river. The River water are also known unable to flow by gravity caused of rob dikes that isolated the river water to flow into the sea, therefore to prevent overflow in the river needs and to prevent overflow and to control the river water level a pumping in rivers.Keywords: Hydrological Simulation, River Flood Control, Storage Evaluation, Pumping
Warfare Robot

These robot used in military are usually employed with the integrated system, including video screens, sensors, gripper and cameras.Android application controlled WARFARE ROBOT, built in with Robotic arm mechanism to pick up or place small objects (like explosives), an on board Wireless video camera, Infrared based surface depth and irregularities perception and android application for movement and other controls of the Robot.The robot will serve as an appropriate gadget for the defence sector to reduce the loss of human life.
Optimasi Kualitas Citra Radiografi Abdomen Berdasarkan Body Mass Index dan Tegangan Tabung pada Computed Radiography

Background: Computed Radiography is a modality of radiographic processing that has wide-exposure latitude. So that in radiographic examination with CR modality is less concerned with the accuracy of the exposure factor especially in the use of tube voltage that can affect the image quality. Image quality determines the accuracy of diagnosis of an illness. Abdominal examination is often done in the radiological installation of Hospital, with different patient conditions especially in Body Mass Index the use of tube voltages tends to vary so as to affect the quality of radiograph and received dose PatientsMethods: This type of research is analytical observational research with cross sectional research draft. Samples in research amounted to 108 distributed in each group of Body mass Index (underweight, normal, overweight) respectively 36 samples. Each body mass index group sample is taken from the use of tube voltages that are often used in RS 75 kV, 80kV, 85 kV and 90 kV respectively as much as 9. Assessments include the value of the exposure index, Noise and anatomical information. Statistical data analysis with Spearman test and univariat.Result: There is a significant connection between the use of cylinder voltage with an index exposure, noise and anatomical imageryConclusion : Optimised tube voltage for Body Mass index Under 75 kV, normal BMI 80 kV and BMI over 85 kV
Intelligent Tutoring Systems’ Measurement and Prediction of Students’ Performance Using Predictive Function

Students' performance in online learning has gained new interest due to the adaptation of Artificial Intelligence into the education sector.The Intelligent Tutoring System's evolution is still ongoing with continuous enhancement being embedded inside the system with various purposes related to learning measurement and performance evaluation.We have introduced a predictive function to determine the students' performance with respect to their peers.The enhancement will enable the system to predict the performance of the students based on the students' performance history and utilization of the intelligent system.Various data will be captured by the system to supply input to this predictive function.The new function considers time of independent study, confidence level during assessment, correctness of answers and average answering time for prediction.From the experiment and analysis conducted, we conclude that the new proposed predictive function is very accurate in predicting students' future performance.