Reviewerly/exHarmony huggingface dataset:
Neural Network for Breast Cancer Prediction

The aim of this study was to establish a method for breast cancer prediction, mainly based on blood test results.The study utilized data collected from 166 participants, half of whom were diagnosed with breast cancer.The data consist of information like the body mass index (BMI), age, and results of seven blood tests that encircle homeostatic model assessment (HOMA) and analyses of glucose, insulin, leptin, adiponectin, resistin, and monocyte chemotactic protein 1 (MCP-1).These seven blood tests are considered as indicators of breast cancer.However, this study was intended to identify the particular parameters of these that are most indicative of breast cancer.Two experiments were conducted by using the feed-forward, back-propagation artificial neural network.The first experiment considered all the foregoing nine parameters while the second experiment was run on only seven parameters because a decision tree for feature selection identified seven of the original nine parameters as strong predictors of breast cancer.Values of the prediction accuracy were 93.1% in the first experiment with nine predictors and 94% in the second experiment with seven predictors.Moreover, specificity and sensitivity were calculated to assess the performance of the proposed method with the two sets of predictors.The indications are promising.As such, this study has the contribution of providing rapid, non-invasive means of breast cancer prediction for early treatment.
Adaptive vector greedy splitting algorithm

We introduce a new transform through a construction that we have called the Adaptive Vector Greedy Splitting algorithm. The main idea behind this algorithm is an optimization step based on the simple Bathtub Principle. We use the Vector Greedy Splitting algorithm to build orthonormal bases for a given vector of random variables (also called signals). A particular basis constructed in this way may be used for signal coompression, audio pattern recognition and other applications of signal processing. We compare performance of the Vector Greedy Splitting algorithm with the Haar wavelet transform applied to the same vector of input signals. The implementation of the algorithms and statistics accumulation are made using the ANSI C computer language and Matlab. The work uses advanced methods of Computer Engineering and Digital Signal Processing.
The Feasibility of Facebook Group Feature as an Alternate Tool in Relevance to Learning Management Systems

As nursing schools embrace e-learning during and beyond the COVID-19 crisis, academic nursing institutions must first assess the attitude or perception of students to achieve successful transition and transformation to e-learning.However, there is limited information about the psychometric properties that measure the attitudes towards e-learning among nursing students in the Philippines.The objective of the study is to determine the validity and reliability of the attitudes towards e-learning scale Filipino nursing students.This descriptive cross-sectional study included 111 Filipino nursing students in selected public and private universities were selected through purposive sampling.The attitude toward e-learning scale was assessed for construct validity through exploratory factor analysis using varimax rotation and internal consistency reliability using Cronbach's á and -item-total correlation.The nineitem attitude towards e-learning scale was loaded on one component with factor loadings ranging from -0.907 to 0.893, explaining 61.92 percent of the total variance.The KMO test revealed a value of 0.900, while Bartlett's test was 644.380 (df =55, p=0.000).The corrected item-total correlation of each item varied in between 0.409 to 0.854, and the Cronbach's alpha coefficient of the 9-item scale was 0.917.The 9-item instrument was valid and reliable in assessing the attitude towards e-learning among nursing students.Further studies should be done in order to test it among varied health science students.
Generating Vowel Nasality for a Rule-Based Bangla Speech Synthesizer

Bangla is a useful language to study nasal vowels because all the vowels have their corresponding nasal vowel counterpart. Vowel nasality generation is an important task for artificial nasality production in speech synthesizer. Various methods have been employed by many researchers for generating vowel nasality. Vowel nasality generation for a rule-basedspeech synthesizer has not been studied yet for Bangla. This study discusses several methods using full spectrum and partial spectrum for generating vowel nasality to use in a rule-basedBangla text to speech (TTS) system using demisyllable. In a demisyllable based Bangla TTS 1400 demisyllables are needed to be stored in database. Transforming the vowel part of a demisyllable into its nasal counterpart reduces the speech database size to 700 demisyllables. Comparative study of the e
Research on the Application of Capacitive Fast Gesture Recognition System

Gesture recognition, compared with the technology of machine vision and image processing, its algorithm is complicated, and the cost is relatively high. In this paper, a gesture recognition system based on the capacitive sensor was designed. The sensor plane of the system gesture data acquisition part was designed by capacitance-related principle. The collected data was subjected to the sliding average filtering processing and multiple training. The fuzzy partition simple template matching algorithm was adopted to quickly identify and recognize the collected specific gesture. Through practical experiments and tests, the total time of the system gesture recognition is less than 1 second, and the average recognition accuracy rate is 97.1%, which meets the system requirements of the real time and accuracy.
Ensemble Hybrid K- Means and DBSCAN Clustering Algorithm – HDKA for Cancer Dataset

Data Mining is the foremost vital space of analysis and is pragmatically utilized in totally different domains, It becomes a highly demanding field because huge amounts of data have been collected in various applications. The database can be clustered in more number of ways depending on the clustering algorithm used, parameter settings and other factors. Multiple clustering algorithms can be combined to get the final partitioning of data which provides better clustering results. In this paper, Ensemble hybrid KMeans and DBSCAN (HDKA) algorithm has been proposed to overcome the drawbacks of DBSCAN and KMeans clustering algorithms. The performance of the proposed algorithm improves the selection of centroid points through the centroid selection strategy.For experimental results we have used two dataset Colon and Leukemia from UCI machine learning repository.
PREVENTION OF PHISHING ATTACKS: A THREE-PILLARED APPROACH

This paper presents a three-pillared strategy for the prevention of phishing attacks.Phishing is a deceptive method of creating and distributing emails and/or websites that attempt to fool users into sharing sensitive financial or identification information.Current literature agrees that these scams can be highly damaging to companies, their employees, and their stakeholders.Unlike traditional scams, though, the Internet adds a layer of anonymity and even invisibility, making it far more difficult to identify the source of the scam, or, in some cases, masking the fact that a scam has been perpetrated.In this paper, we first review information about tactics that can effectively reduce the success rate of phishing attempts.We then formulate a three-pillared prevention strategy based on: (1) one-time passwords, (2) multi-level desktop barrier applications, and (3) behavior modification.By utilizing this approach, individuals and organizations should be better able to protect their information and decrease the damage caused by phishing attacks.
Efficient Scheduling Mechanisms for Secured Cloud Data Environment

Big Data has won heaps of intrigue in light of the tremendous degree it presents for a higher comprehension of the business and decision making strategy for an enterprise. There has been a broad scope of looks into happening far and wide on this to make the most out of it and entirely some of specialized stages are to be need to the analysis for enormous records . However, Scientists and organizations had the option to expand frameworks that could deal with the enormous measurements and perform investigation on it, the security segments remain immensely immaculate. And misfortune can be a reason for significant data loss while applying hybrid security. As if information changes, then it is not valuable. So here is a need to adopt proper scheduling mechanism in cloud computing to avoid process delay. This paper expounds some encryption strategies and proper scheduling mechanisms. Hyper elliptic curve cryptography (HECC) towards verified enormous record analyses is embraced here to describe with proper scheduling
Gold, radioactive and associated mineralization in Gabal El-Maghara Coal, Sinai, Egypt: Mineralogy and geochemistry

Gabal El-Maghara is located in North Sinai, Egypt.The exposed sedimentary succession is 200 m thick and consists of six formations.Economically, the Safa Formation (160-215 m thick) is the most important rock unit and contains more than 11 coal seams, two of which are minable; namely: The Main Coal Seam (MCS) and the Upper Coal Seam (UCS).The former is ~130 to 190 cm thick and has an estimated coal reserve of about 52 million tons.The Jurassic El-Maghara coal, contains a large mineral assemblage of uranium (betafite, plumbobetafite, kasolite and zippeite) and thorium (thorite and uranothorite) in addition to gold.Zircon, fluorite and barite were also recorded.These minerals are allogeneic (detrital) and, less commonly, authigenic.Allogeneic minerals were inherited from crystalline rocks in Sinai and especially older hydrothermal deposits that were formed at wide ranges of temperature and pressure.On the other hand, some base metals (pyrite, chalcopyrite, galena, nickel, zincite, scheelite and cuprite) in El-Maghara coal were formed authigenically by: (i) precipitation from circulating underground waters; and / or (ii) by the action of marine waters during periods of the post-Jurassic sea transgressions.Gold (0.5 gm/ ton) was originally precipitated from mesothermal and Gold…
M-LEARNING DEVICE: USING VIDEO TO IMPROVE STUDENTS’ WRITING SKILL

Mobile learning devises like; mobility, flexibility, availability, supporting all kinds of communications, and interaction between teacher and students. The use of mobile learning has an effect on learning the era of industrial revolution 4.0 that touches the virtual world, human connectivity, machine, and data, known as the Internet of Things. The objective of study was to use video based mobile learning on students’ writing skill. The participants of the study were 80 students at grade 10 SMK 4, Malang. They were asked to write an essay after watching a video. They stated that using video based mobile learning was helpful in improving the quality of their writing both content and techniques. The usage of mobile learning is necessary to improve students’ writing skill and their thinking skills.
Klasifikasi Teks Pengaduan Masyarakat Dengan Menggunakan Algoritma Neural Network

The development of the Internet led to a flood of digital information. Various information can be obtained easily just by clicking or pressing 'enter'. Dissemination of information in the form of digital documents has experienced unprecedented growth. One of the web intended for the general public in the province of Central Java, web named 'Lapor Gub!'. This site was made with the intention to accommodate the aspirations, concerns, or complaints against public services and also dissatisfaction with the performance of local government and the provincial government of Central Java. An increasing number of documents in text format significantly recently made the process of grouping documents (document classification) becomes important. By using the method of text classification, then the document is an overwhelming number of these are organized in such a way so as to facilitate and accelerate the search needed information. Experiments in this study aimed to classify the Indonesian language text documents using Neural Network algorithm. The test is done by using a sample of text documents taken from a web-based electronic mass media entitled 'Lapor Gub!'. The experimental results show that the neural network method effectively used to classify texts public complaints. It is seen from the experimental results, namely the use of Neural Network algorithm on the classification process produces high accuracy in the amount of 43.00% with a period of 03 hours 45 minutes 14 seconds of the Indonesian language text documents classify text public complaints
THE DESIGN OF INFORMATION SYSTEM FOR CAD DIVISION IN GARMENT FIELD WITH WATERFALL METHOD

Sukabumi Regency has various industrial or garment companies scattered in several regions. One of them is PT. Yongjin Javasuka Garment, which is located in the cicurug sub-district, can support the economic pace of local residents by opening jobs as laborers. In the world of garment, there are many organizations that are active in every field. The CAD is a division that works as a support role to launch a production process from start to finish. For his work in this division, he has a lot of expertise and several different sections, namely an adm, pattern maker, marker, plot pattern and plot marker. When working, everything interacts and relates to each other. Through the internet, all computers can access each other's data. However, in practice now there are still obstacles that should be evaluated jointly. Data provided from one employee to another is still manual. This causes the work to be less effective, slow and thus not optimal. In the above case, we need an information system that can accommodate data with internet services to maximize the company's facilities. Easy, fast and integrated information system. Therefore, in this study, the waterfall method is used to make it easier for employees while working in accessing data.
Nonparametric Statistical Models

A summary is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
A discriminative analysis framework for multi-modal information fusion

Since multi-modal data contain rich information about the semantics presented in the sensory and media data, valid interpretation and integration of multi-modal information is recognized as a central issue for the successful utilization of multimedia in a wide range of applications. Thus, multi-modal information analysis is becoming an increasingly important research topic in the multimedia community. However, the effective integration of multi-modal information is a difficult problem, facing major challenges in the identification and extraction of complementary and discriminatory features, and the impactful fusion of information from multiple channels. In order to address the challenges, in this thesis, we propose a discriminative analysis framework (DAF) for high performance multi-modal information fusion. The proposed framework has two realizations. We first introduce Discriminative Multiple Canonical Correlation Analysis (DMCCA) as the fusion component of the framework. DMCCA is capable of extracting more discriminative characteristics from multi-modal information. We demonstrate that optimal performance by DMCCA can be analytically and graphically verified, and Canonical Correlation Analysis (CCA), Multiple Canonical Correlation Analysis (MCCA) and Discriminative Canonical Correlation Analysis (DCCA) are special cases of DMCCA, thus establishing a unified framework for canonical correlation analysis. To further enhance the performance of discriminative analysis in multi-modal information fusion, Kernel Entropy Component Analysis (KECA) is brought in to analyze the projected vectors in DMCCA space, and thus forming the second realization of the framework. By doing so, not only the discriminative relation is considered in DMCCA space, but also the inherent complementary representation of the input data is revealed by entropy estimation, leading to better utilization of the multi-modal information and better pattern recognition performance. Finally, we implement a prototype of the proposed DAF to demonstrate its performance in handwritten digit recognition, face recognition and human emotion recognition. Extensive experiments show that the proposed framework outperforms the existing methods based on similar principles, clearly demonstrating the generic nature of the framework. Furthermore, this work offers a promising direction to design advanced multi-modal information fusion systems with great potential to impact the development of intelligent human computer interaction systems.
The Use of Audiovisual Media to Improve the Speech Skills of Private School Students

&lt;em&gt;This study aims to improve speaking skills through the use of audiovisual media in class IB SD Negeri Suro, Kalibagor District. Speaking skills observed in this study include linguistic and non-linguistic aspects. In the linguistic aspect, it consists of stress, speech, vocabulary / diction, and sentence structure, while in the non-linguistic aspect it consists of fluency, disclosure of speech material, courage and attitude. This type of research is Classroom Action Research (CAR) which consists of 2 cycles. The subjects of this study were 22 students of SD Negeri Suro class IB. The research design used includes the planning, implementation, observation, and reflection stages. The data collection instruments used were performance tests, teacher and student observation sheets and documentation. The data analysis technique used is descriptive quantitative data analysis. This study shows that the use of audiovisual media can improve the speaking skills of grade IB SD Negeri Suro students in terms of process and outcome. The process improvement is seen from the average results of student activity observations which are included in the good category in the first cycle with a percentage of 45.5% increasing in the second cycle to 68% and are included in the very good category. In terms of results, it is evidenced by the average value of students' speaking skills in pre-action 67.54 increased to 73.05 in cycle I and increased to 75.64 in cycle II. &lt;/em&gt;
Optimally weighted local discriminant bases : theory and applications in statistical signal and image processing

This thesis is concerned with Local Discriminant Basis (LDB) algorithm, its properties, optimization and applications in feature extraction and classification. LDB algorithm targets features extraction from redundant dictionaries such as wavelet packets or local trigonometric bases at low computational complexity. As the main contribution of this thesis, an optimization process is introduced to further improve the accuracy of the overall scheme in applications when a region of interest can be specified by the experts in the field of application (based on LDB selected features) to further characterize signal classes in smaller regions. Audio signal and textured image classifications are practical applications that are studied in this thesis to test the efficiency of optimally weighted local discriminant basis algorithm (OLDB) as a feature extraction scheme. Various properties of the algorithm such as noise behavior and stability analysis are studied from an engineering perspective. The implementation aspects of the algorithm in one dimension are reviewed as well as in two dimensions that serve as implementation guidelines.
Recovering Evidentiary E-mail for Non-Repudiation Forensics

Computer Forensic, the upcoming branch of forensic science where acquiring, preserving, retrieving and presenting content processed electronically and stored digitally, is used for legal evidence in computer related crimes or any other unethical practice involving manipulation of digital content. Such digital content can take many forms which are manifested by different file formats and digital artifacts”. This paper concentrates on acquisition of deleted e-mail from mailbox of web servers satisfying two tier, three tier and n-tier technology. A detailed survey of several possibilities are included for non-repudiation forensic. A case study of a particular file type using suitable forensic tool is cited as a proof of concept towards this claimed inference to provide digital evidence in case of non-repudiation by sender and/or by receiver. This is simply conducted by using Encase a proprietary Digital forensic tools. The whole process is captured in step by step fashion to have a better understanding of the mechanism used. Recovery of files/emails have certain kinds of legal hurdles, the paper have addressed them as well. This paper contributes to the extend the recovered email can used as a ready digital evidence in any court of law.
Thermal control system based on ant colony algorithm

In order to improve the accuracy and speed of solving the inverse problem of source-seeking heat conduction, the paper proposes a correlation-based ant colony optimization algorithm for the inverse problem of source-seeking heat conduction based on the characteristics of the influence of the heat source position on the boundary temperature distribution in the heat conduction problem. This method is used to construct the corresponding heuristic information value for each co-ordinate of the heat source location, which can reflect the degree of similarity between the temperature curve of the calculated measuring point and the temperature curve of the real measuring point, namely the correlation degree. The ant colony optimization algorithm the medium path selection mechanism and the structure of the objective function have been improved. The paper replaces the actual experiment with numerical calculation obtain the temperature of the measuring point, and performs computer programming experiment on the inverse problem. The calculation results show that the calibration method of this heuristic information value and the objective function the construction method can distinguish the quality of the path well, thereby increasing the speed of the ant colony converging to the best path. The computational efficiency is improved by 18-60% compared with the ant colony algorithm that does not consider the correlation.
Achieving Application Portability In Cloud Computing Environment

Application portability in a cloud computing condition is characterized to be the exchange of utilization or its segments starting with one cloud administration then onto the next cloud administration of its specialized comparability. The application should be such transferred that it does not make any significant changes in the application code although recompilation or relinking may be allowed. Application portability can be performed by the customer only in case of IaaS and PaaS services because for SaaS the application code relies on the cloud service provider itself. The application artifacts and their dependencies play a major role in the target environment. Containers and their related infrastructure hold valuable importance for application portability. In this paper, we will elaborate on all the issues, challenges, scenarios, and approaches for application portability that rose to date. We will study all the facets of data and application portability such as its instruction, syntax, metadata, and policies. The evolution of automation tools and technologies has made the portability of applications easier as the manual processes were inefficient and error-prone.