bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Evaluation of Novel Convolution Neural Network Architecture for Melanoma Skin Cancer Diagnosis on Different Hardware Processing Units
Authors: Qureshi Mohammad Naved , Umar Mohammad Sarosh 
Published: August 2021

Abstract
Skin cancer is one of the major public health concerns among the white population with more than a hundred thousand cases every year. Melanoma is one of the deadliest forms of skin cancer which is responsible for thousands of deaths in US alone in recent years and, therefore, early diagnosis is very important to increase the survival rate of melanoma patients. In last few years’ Deep neural networks have been utilized by researchers to build best models for classifying or diagnosing skin cancer. In this paper Deep neural network-based CNN architectures to classify Melanoma is proposed. The CNN architecture proposed in this work is implemented on CPU, GPU and TPU and the performance of the model is shown on all these platforms. The proposed model is compared to other works done so far for melanoma diagnosis in terms of various performance metrics like prediction accuracy, specifity, sensitivity and it is observed that the proposed models outperformed them. The dataset utilized in training and testing the proposed models is ISIC archive dataset which contains 4750 skin images for two classes i.e. melanoma and benign. The results of our study have proved that utilizing GPU and TPU speeds up the training 38 times faster than CPU and can accelerate the performance of CNN for features extraction, optimization and classification of skin cancer images and the proposed model has outperformed the other models compared with it.
Potential applications of thermal spray coating for I.C. engine tribology: A Review
Authors: Sonia  , Walia R.S. , Suri N.M. , Chaudhary Sumit , Tyagi Ankit 
Published: August 2021

Abstract
The IC engine has been a conventional source of producing mechanical energy for various applications. The tribopair of piston ring and cylinder liner has to work in adverse tribological conditions such as high temperature, speed and load. The wear of piston ring not only reduce the performance of the tribopair but also cause the failure of the system. The insufficient lubrication at the interface increases the tribological losses drastically. The tribological losses can be minimized by coating of some material which has superior tribological performance. The thermal spray coating can be used for providing a stable coating over the tribological surfaces. The versatile operating conditions and process parameters assist the deposition of required material to a desired thickness over the various substrate materials. The process can be used for producing the stable coating over at a faster deposition rate. The environmental friendly coating material can be used as a bled with the conventional coating material for making the process sustainable.
Multi-Characteristics Optimization in Micro-milling of Ti6Al4V Alloy
Authors: Roushan Ashutosh , Srinivas Rao U. , Patra Karali , Sahoo Priyabrata 
Published: August 2021

Abstract
Nowadays, Ti6Al4V alloy is widely used to fabricate miniaturized prostheses, orthopedic and dental implants. Surface quality is an essential aspect of the implant parts owing to its effect on service life, which could be achieved through a proper selection of precise micromachining parameters. In this research, the mechanical micromachining of Ti6Al4V alloy has been performed, and optimized process parameters are considered for analysis. Surface roughness (Ra) of the machined microchannels, cutting force in the feed direction (Fy), cross-feed direction (Fx), and vibration on the top surface plate along the spindle axis (a z) have been taken as the response variables and were analyzed. The tests were accomplished at various rotational speed (N), feed per tooth (f z), axial depth of cut (a p), and cutter diameter (D) as machining parameters, each at three levels. The significant factors and their levels are identified using analysis of variance (ANOVA) and S/N ratios of response plot. According to the GRA test, N : 15000 rpm, fz : 0.2 µm/tooth, ap : 30 µm, and D : 200 µm are optimal parameters setting for better machinability of Ti6Al4V. ANOVA results indicated that feed rate is the most influential factor for lower cutting forces, surface roughness, and workpiece vibration. Fast Fourier transform (FFT) analysis depicts the stiffness of different micro end-mills significantly affect the machining performance like vibration amplitude during the micro-milling of Ti6Al4V alloy.
Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features
Authors: Zakariya S. M. , Jamil Mohd Atif 
Published: August 2021

Abstract
Image retrieval is a procedure of finding appropriate images in the image database. There are two types of image retrieval systems in common practice. These are the text-based image retrieval (TBIR) system and content-based image retrieval (CBIR) system. The content based system is proven to be more effective in which the visual contents of the images are extracted and described by multi-dimensional feature vectors. In this work, several models are developed by combining different image features in a combination of two and three. To begin with, three different models based on the combination of two features, viz., color with shape, shape with texture, and color with texture are designed. A three features based model is considered with color, shape, and texture in the next step. The retrieval rate of the mentioned models is assessed in terms of precisions. The results are obtained using COREL standard database. This study shows that the images can be better retrieved using three features based model in contrast to models using two features.
Performance Analysis of Complex Manufacturing System using Petri Nets Modeling Method
Authors: Kumar Amit , Kumar Vinod , Modgil Vikas , Kumar Ajay , Sharma Anita 
Published: August 2021

Abstract
The Petri nets modeling method is a powerful tool for the performance analysis of industrial systems. The main factor which makes it more effective among the various popular simulation methods is the ability to deal with the real working conditions. The data obtained from such a manufacturing system is generally full of uncertainties and the Petri nets modeling method deals with this data to reflect the real behavioral pattern of different sub-systems installed in the plant. The Petri nets-based simulation method provides the availability of the sub-systems for a long time period by running the plant in virtual manners. The results obtained through the analysis can be utilized to identify those sub-systems which highly affect the system availability and separate maintenance planning for each sub-system can reduce the production loss due to unavailability of any sub-system for performing its intended task. The proposed methodology has been demonstrated in this paper using a complex repairable manufacturing system.
A Multi-Objective Approach for Software Quality Improvement
Authors: Makkar Priyanka , Sikka Sunil , Malhotra Anshu 
Published: August 2021

Abstract
Software industry always demands high-quality software that must be reliable, robust, flexible, reusable, effective, and extendable. To improve software quality refactoring is an efficient and frequently opted technique. Most of the refactoring work has been done on the source code level less work has been done on model-based. Model base refactoring is more difficult to estimate. In this research paper, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is applied to find how software quality is affected by refactoring technique. It impacts the software quality in both positive as well as negative way.
Comparison of Fly Ash Based (AAC) Block and Clay Bricks for Structure and Strength Properties
Authors: Singh V , Behl V , Dahiya V 
Published: August 2021

AbstractFly Ash, a waste product collected from precipitators of coal based thermal power plants can be utilized as a potential resource as construction material. Fly ash, being fine light weight silica particles can cause environmental pollution if not managed properly, and/or not utilized as construction material. Fly ash can be used along with cement, cementitious materials to develop bricks and blocks which possess good properties for civil engineering structures. This paper presents comparative analysis of clay bricks and fly ash based autoclaved aerated concrete (AAC) block properties, methodology, process and material required for preparing fly ash based AAC block as per Indian standard code provisions, inspection and quality control. The textures of the blocks with fly ash were very similar to that of clay bricks; the sample with the additive contains spherical fly ash particles. These fly ash particles led to reduction in the density of the blocks and a substantial improvement in their durability. Use of this additive could have practical implications as a means of recycling and for achieving cost savings in blocks production. The structural cost, cement mortar for plaster & masonry, breakage, construction speed, quality, fitting & chasing, carpet area, availability, energy saving, chemical composition of fly ash based AAC blocks compared with normal clay bricks that delivered good results. From the present study, it can be concluded that fly ash based AAC blocks can be used as an alternative to clay bricks because of their strength, uniformity and smooth surfaces and lower cost. The cost of fly ash-based blocks was found about 20-35 % lesser than the clay bricks.
Internet of Things (IoT) based Smart Helmet for Construction
Authors: Kuhar Preeti , Sharma Kaushal , Hooda Yaman , Verma Neeraj Kumar 
Published: August 2021

Abstract
In India, the use of helmets is mandatory at construction sites as per the government rules. The Use of a helmet protects the labor head against the impact of the falling object from any height. There was a modification in the ordinary helmet into a smart helmet by adding the Work Mode (ON/OFF) system, GPS System, Smoke Sensor, Task Completion Button. In this, the helmets will automatically be updating the work mode ON, if someone wears it because a clamp turns the system ON and information goes to the supervisor. Same as this, there are two more buttons which indicate the task completion and about the emergency. Using all these buttons and GPS Systems helps the labor in many ways and manage the tasks and time also. For this, GPS and an IOT System are being used for the functioning of a helmet. Real-time signals will be sending constantly with the help of installed various sensors to monitor. The main objective of this study is to activate the installed components so that the various emergencies can be detected at the supervisor’s room or to find the location of the worker, to keep an eye on the various field tasks. It will give the actual location of the worker and in case of emergency; the labor can send his/her exact location to the command office of the construction site. For this, HC12 technology is chosen to give the information by sending pinpoint location, HC12 module which has a GPS module. These components are installed in the ordinary helmet and connect all these with a single computer so that all the activities are to be supervised in a single computer. By installing all the components in one construction helmet, the internet of things based smart helmet is assembled.
Disease Single Nucleotide Polymorphism Selection using Hybrid Feature Selection Technique
Authors: Phogat Manu , Kumar Dharmender 
Published: August 2021

Abstract
According to recent studies the Single Nucleotide Polymorphism (SNPs) plays very important role as genetic marker in various complex diseases. Lots of machine learning techniques are already applied on SNPs data to distinguish between affected and healthy individuals. The major problem with the SNPs dataset is high number of features and small number of samples which are referred as ‘large p’ and ‘small s’ problem. In this paper we proposed a hybrid feature selection method for selecting an optimal subset of SNPs and from that we select the significant SNPs, which act as marker for disease. The method is a hybrid technique based on combination of filter and wrapper method, the (mRMR) Minimum Redundancy Maximum Relevancy and Particle Swarm Optimization for Gene Selection with Support Vector machine (PGOGS-SVM) respectively. The proposed mRMR+PSOGS-SVM approach has been applied to mental retardation SNP dataset taken from NCBI-GEO website. The method has achieved high classification accuracy up to 88% and outperformed all other compared feature selection techniques.
Machine Learning based Heart Disease Diagnosis using Non-Invasive Methods: A Review
Authors: Kumar Narender , Kumar Dharmender 
Published: August 2021

Abstract
Heart disease is the most dangerous disease among all the non-communicable diseases. Annually 17900 thousand of peoples die due to heart problems. Cardiovascular disease (CVD) is the general term used for most of the heart diseases. There are two types of methods for diagnosing a CVD: (i) Invasive Methods (ii) Non-Invasive Methods. Coronary angiography is an invasive method for diagnosing a CVD which is a costly, painful and complicated process. A variety of Non-Invasive (NI) methods are available for diagnosing a CVD. NI methods generate a lot of data which is mainly of 3 kinds :(i) data based on clinical parameters, lab tests and symptoms (ii)data based on raw heart signals (ECG and PCG) (iii)data based on heart images. Majorly, three different machine learning (ML) frameworks may be developed based on the 3 types of data. First framework is simple and main concern is feature selection and classification. Second and third framework is complicated and requires a lot of techniques (preprocessing, segmentation and feature extraction) prior to classification of heart signals and images respectively. In this paper a comprehensive review is presented that summarizes some recent and prevalent machine learning methodologies in all the frameworks. Most of the papers reviewed in this study are from IEEE Explorer, Science Direct, PubMed, Springer, Hindawi, ACM digital library and MDPI libraries. It is found that Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are superseding in most of the studies in all the frameworks. Deep neural network is comparatively newer machine learning methodology which is giving prominent results in classifying heart sound signals and cardiovascular images. The present study will help to automate diagnosis process of heart disease by providing guidelines and avenues to new researchers in domain of machine learning.
Hyperspectral Image Classification Using Deep Learning Models: A Review
Authors: Kumar Deepak , Kumar Dharmender 
Published: August 2021

Abstract
Hyperspectral image (HSI) classification is one of the important topic in the field of remote sensing. In general, HSI has to deal with complex characteristics and nonlinearity among the hyperspectral data which makes the classification task very challenging for traditional machine learning (ML) models. Recently, deep learning (DL) models have been very widely used in the classification of HSIs because of their capability to deal with complexity and nonlinearity in data. The utilization of deep learning models has been very successful and demonstrated good performance in the classification of HSIs. This paper presents a comprehensive review of deep learning models utilized in HSI classification literature and a comparison of various deep learning strategies for this topic. Precisely, the authors have categorized the literature review based upon the utilization of five most popular deep learning models and summarized their main methodologies used in feature extraction. This work may provide useful guidelines for the future research work in this area.
Analysis of Engineering Students’ Understanding in Differentiate Derivative and Integral
Authors: Funny R A 
Published: August 2021

Abstract
Derivative and integral are such fundamental concepts for Engineering students for their advanced coursed. These two concepts are opposite each other, since Integral as an antiderivative of a function f(x) is a function, whose derivative is equal to f(x). However the Engineering students still confused to identify the difference between derivative and integral. This study aimed to examine how students distinguish the understanding between derivative and integral in a sample of second-year aerospace engineering students in one of the private universities in Yogyakarta, Indonesia, and how they solve it. These two concepts have been taught in their high School and first-year in university. The instrument of this study applied the same problems for two questions, to derived and also to integrated, in order to find students understanding in distinguishing the concepts. Qualitative research was chosen as it can describe the students thinking in answering the test. The result reveals how students differentiate solving Derivative and Integral. One-third of these students do not use the symbol of derivative or integral in solving the question. Most of them just use one symbol, which the majority is derivative, the rest do not use any symbol. Using symbol helps students to answer correctly when dealing with two opposite concepts such as derivative and integral. Thus, conceptual and procedural understanding play a further important role. Integral procedures appear to be avoided because of the complex formulas that involve fractions.
Developing the scripting task for mathematical connection between the university and school mathematics content
Authors: Pramasdyahsari A S 
Published: August 2021

AbstractThe pre-service mathematics teachers (PMTs) found abstract algebra is difficult and irrelevant to the secondary teaching. Even though several studies have elaborated the connection between them, they cannot explicitly use its connections. Therefore, the study aimed to describe the development of a scripting task design that encourages the secondary mathematics pre-service teacher to use the mathematical connection from abstract algebra to solve the school mathematics problem. The study involves the educational research and development cycle refers to Borg and Gall’s model, which is adjusted to the need of this study. The research procedures consist of three main stages: collecting data, planning, and developing the product. In designing the scripting task, several task principles were involved, such as necessity, two-way synergy, interest, focus, awareness, and self-reflection. The scripting task was developed through at least two considerations that are accomplishing both professionally relevant and mathematically rigorous goals and the relevance of topic for both the university and school mathematics. There is a significant need for further studies for implementing this scripting task to see teachers’ awareness in using the mathematical connection from university mathematics to help their students solve school mathematics problem.
Variation theory-based mathematics teaching: The new method in improving higher order thinking skills
Authors: Baskoro I 
Published: August 2021

Abstract
Variation theory-based mathematics teaching emphasizes variation as necessary condition for learners to be able to discern new aspects of an object of learning. The study aims to describe whether the variation theory-based mathematics teaching is more effective than mathematics teaching using scientific approach in terms of higher order thinking skills of vocational school. This study is quasi-experimental research whose population is all grade X, students of Cipta Bhakti Husada Vocational School. The data collecting instrument in this research was a test, consisting of the higher order thinking skills questions. The result of the study at significance level of 5% show that the variation theory-based mathematics teaching is more effective than mathematics teaching using scientific approach.
Mathematics teacher professional development program: fostering the skills for teaching meaningful mathematics through active learning during pandemic era
Authors: Nurhasanah F , Usodo B , H Ekana C , Kuswardi Y , Sutopo  , Lestari S 
Published: August 2021

Abstract
This is a report of a community service program for mathematics teachers. This program was held for fostering mathematics teachers’ skills in teaching meaningful through active learning using Google Classroom flat form. The program takes place in the Karanganyar region, Surakarta, Indonesia. The participants of this program were junior high school mathematics teachers from Karanganyar district. They were 28 participants. The workshop was held for three days: two days were face-to-face workshop and one day for the online workshop. There are four main topics for the workshop: (1) active learning; (2) Mathematical Abstraction and Mathematical Skills; (3) Developing Question and Students Worksheet; and (4) Designing Active Learning on Google Classroom. The development of offline and online modules in the workshop was based on the theory of I-CARE model. I-CARE Model comprises of five components: Introduction, Connection, Application, Reflection, and Extension. The module consisted of a package for doing workshop which is: Powerpoint slides for offline and online courses; word documents for explaining the structure of the workshop and the worksheet of the participants; a Google classroom for online activity. Due to the pandemic session, the activities of discussion during the workshop were held on online flat form in Google Classroom.
Pre-service teachers’ integrated curriculum approaches to STEM education in classrooms
Authors: Jun-On N , Kaya J 
Published: August 2021

Abstract
This study aims to explore the integration approach employed by pre-service teachers in their STEM lessons. In-depth interviews with six pre-service mathematics teachers and ten pre-service sciences teachers from Lampang Rajabhat University were employed. The interviews were analyzed using the conceptual framework of the curricula integration model described by Robin Fogarty, focusing on the approaches used by pre-service teachers to STEM education. The study revealed that most participated pre-service teachers use connected and shared approaches to STEM education, and they only appeared if standard textbooks provide a guideline. The use of the integrated approach across disciplines by pre-service teachers is infrequent. This study also revealed that pre-service teachers reject subject-integration in their classroom as well as the necessity of implementing technology in their teaching because of time limitations. The findings of this study possess practical importance that they can be utilized for the modification of teacher education and training programs in Lampang Rajabhat University. Consideration of the findings may contribute to the development of the Thailand National Core Curriculum and its objectives. Additionally, there was a contribution to add literature to STEM education in Thailand.