bluuebunny/crossref_metadata_2025 huggingface dataset:
A new Connected Component Analysis based System for Text Segmentation in Degraded Historical Document Images
Authors:   Assistant Professor, Electronics and Communication Engineering Department, Kongu Engineering College, Perundurai, Erode, India., Narayanan* Mr.V.Sathya , Kasthuri Dr.N. ,   Professor, Electronics and Communication Engineering Department, Kongu Engineering College, Perundurai, Erode, India., Dharani T. ,   Student, Electronics and Communication Engineering Department, Kongu Engineering College, Perundurai, Erode, India., Deepa D. ,   Student, Electronics and Communication Engineering Department, Kongu Engineering College, Perundurai, Erode, India.
Published: May 2020

Historical documents contain valuable heritage information. These documents are preserved in the manuscript preservation center and archaeological departments. They are mostly degraded in nature and hence hard to read and understand the contents. So, there is a need for text segmentation and feature extraction to convert these manuscripts into machine editable format. In this work, we present an effective way to segment historical document images into characters. It is a challenging segmentation process due to complex background images. In this paper, horizontal histogram, vertical histogram and connected component analysis is used to segment text documents images. In this algorithm, the input image is converted to gray scale image, then gray image is converted into binary image [Otsu’s method] and then all the objects containing fewer than desired pixels are removed. Line and word segmentation is implemented using horizontal and vertical histogram method respectively. Then the connected components are labeled and properties are measured for the image regions. Connected component analysis is used to segment the characters and the individual characters are extracted. The simulation result shows that the proposed segmentation method achieves an average accuracy of 93.37% for HDLAC 2011 DATASET. Moreover this method is more efficient and more suitable for real time tasks.
Improvised Collaborative Filtering for Recommendation System
Authors:   Ph.D. Scholar, Department of Information Technology, Jagannath University Jaipur, Rajasthan, India., Gupta* Shefali , Dave Prof. (Dr.) Meenu ,   Ph.D., Department of Computer Science, Jagannath University Jaipur, Rajasthan, India.
Published: May 2020

Collaborative filtering (CF) is one of the most important techniques of recommendation system and has been utilized by many e-commerce businesses to provide recommendation to its users. This paper sheds light on CF and its methods. This paper demonstrates a practical algorithm by leveraging data on user ratings for mobile phone devices and then provides recommendations to the target user based on the ratings given by similar users. It also elaborates an algorithm of CF that overcomes some of the common limitations faced by other algorithms. To explain the methodology of collaborative filtering this research paper looks at mobile phone data, especially the mapping of users (buyers) and the rating they provide for mobile phones they purchase. The model first evaluate multiple collaborative filtering techniques (variations of user based and item based filtering) by use of ROC curve and then provide recommendation to the user based on the best identified technique. Collaborative filtering is best utilized where the information on “users” and/or item is limited. For example, you can imagine the hotel booking website that provides recommendation to the website visitor, even though the user has never visited the website before (first time user). In such a situation as the information about user is limited the website algorithms are still able to utilize collaborative filtering methodology to provide recommendations.
Modelling and Controller Design for Non- Linear Two Tank Interacting System
Authors:   Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam., K* Sneha.
Published: May 2020

This paper explains the mathematical modelling and controller design of Two Tank Interacting System (TTIS) for a non-linear process. To design the non-linear process using Matlab Simulink and control the process using conventional PID controller and Fuzzy Logic Controller (FLC). A comparative study was conducted extensively made to examine which controller suits well for the non-linear process through the response observed.
Applying Haar Wavelets in Tasks of Digital Processing of Two-Dimensional Signals
Authors:   Professor, Department Technology, Tashkent University of Information Technology named after Muhammad al-Khwarizmi., Zaynidinov* H.N. , Yusupov I. ,   Assistant, Department of Information Technologies, Tashkent University, Information Technologies Muhammad al-Khwarizmi., Mannopova M.G. ,   Graduate student, Tashkent University of Information Technology, Muhammad al-Khwarizmi.
Published: May 2020

In this article, a system application of local basis functions discussed, defined on compact media. formulas for estimating the accuracy of calculating the spectral energy by the method of two-dimensional Haar wavelets and main concepts of Haar fast transformation, spectral energy of Haar coefficients will be discussed in this article. effectiveness will be shown in order to solve problems of sampling of compact signals. Signals cannot only be functions of time, defined at finite intervals, but also functions of arguments of a different physical nature, for example, distances along surfaces.
Factors Influencing Staffs Turnover in Malaysia Construction Companies
Authors:   Department of Quantity Surveying, Faculty of Built Environment, University Teknologi Malaysia, Johor Bahru, Malaysia,, Islam* Monirul , Mahmud Syamsul Hendra ,   Department of Quantity Surveying, Faculty of Built Environment, University Teknologi Malaysia, Johor Bahru, Malaysia, Mohamed Sarajul Fikri ,   Department of Quantity Surveying, Faculty of Built Environment, University Teknologi Malaysia, Johor Bahru, Malaysia, Zainuddin Muhammad Faeiz Bin Mohd ,   Department of Quantity Surveying, Faculty of Built Environment, University Teknologi Malaysia, Johor Bahru, Malaysia., Alaboud Naif ,   Umm Alqura University, College of Engineering and Islamic Architecture
Published: May 2020

Employee turnover has always been the real issue in the fields of organisational behaviour, human resource management and labour economics. The issue of why people leave their workplace is always a key concern for human resource managers in various industries and their organisations because of the costly effect that comes from it. Although the vast study on employee turnover, the information regarding this issue is very scarce in the scope of construction industry in Malaysia. The purpose of this research is therefore to determine the factors influencing employees’ turnover in construction companies and the strategies taken by the companies to retain employees. A total of 45 sets of questionnaires were collected. The questionnaires collected were then analysed by frequency distribution. For the first objective, the findings revealed that the studied factors influenced employee turnover from a neutral to fair extent. For the second objective, all the strategies mentioned could be taken and used as retention strategies by the company from a neutral or fair extent and hence its practicality and effectiveness to retain employees.
Developing a Household IoT E-waste Management Guideline: Analyzing Data with Atlas.ti 8
Authors:   Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia., Razip* Marym Mohamad , Savita KS ,   Department of Computer nd Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia., Kalid Khairul Shafee ,   Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia., Muniandy Manoranjitham ,   Department of Computer Sciences, Universiti Tunku Abdul Rahman (UTAR), Kampar, Malaysia.
Published: May 2020

The Internet of Things (IoT) plays a huge part in the current Fourth Industrial Revolution which turned out to be one of the fastest contributors towards technological development around the world including Malaysia. As good as it sounds, an emergence of new technology means increases in the number of digital devices which later contribute towards the rise of E-Waste or the possibility of “IoT E-waste” generation if they are not managed properly. Hence, this paper aims to investigate the stakeholders involved in managing a sustainable household IoT E-waste collection processes as well as the elements involved in developing a sustainable system to manage those wastes. The research adopts case study research method where three cases were studied and a total of six participants were interviewed, with two participants per case. Outcomes from the interview were transcribed and analyzed using an Atlas.ti software. The identified stakeholders involved and the elements for a sustainable waste system will contribute towards the development of a Sustainable Household IoT E-waste Management Guideline which particularly enables the policymakers to strategize on the planning, development, and implementation of IoT initiatives for the future of Malaysia household IoT E-waste management.
Sentimental Classification of News Headlines using Recurrent Neural Network
Authors:   Computer Science and Engineering, National Engineering College, Kovilpatti, India., Prakashini* S. , kumar D.Vijaya ,   Computer Science, and Engineering, National Engineering College, Kovilpatti, India.
Published: May 2020

Sentiment analysis combines the natural language processing task and analysis of the text that attempts to predict the sentiment of the text in terms of positive and negative comments. Nowadays, the tremendous volume of news originated via different webpages, and it is feasible to determine the opinion of particular news. This work tries to judge completely various machine learning techniques to classify the view of the news headlines. In this project, propose the appliance of Recurrent Neural Network with Long Short Term Memory Unit(LSTM), focus on seeking out similar news headlines, and predict the opinion of news headlines from numerous sources. The main objective is to classify the sentiment of news headlines from various sources using a recurrent neural network. Interestingly, the proposed attention mechanism performs better than the more complex attention mechanism on a held-out set of articles.
Intelligent Application of WSN for Forest Monitoring
Authors:   Professor & Head Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India., Balamurugan* A. , kumar M.Pranesh ,   Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India., kumar R.Muthu ,   Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
Published: May 2020

A Large destructive fire that spread over a forest or area of woodland is a forest fire and another major problem is entering of wild animals into the cities, farm lands, highway roads that causes loss of humungous amount of Property, Wildlife, Ecosystem and Economy. The project is focused on creating a permanent solution for this problems. It consists of an integrated IoT based WSN (Wireless Sensor Network) system to detect, monitor and solve the issue without any manual involvement. With the help of cloud computing, regular monitoring is achieved. The system uses the latest Microcontroller, Wi-Fi communication and precision sensors to monitor the forest. The system also provides a quick response system for the fire and escaping of animals into the farmlands can be controlled at the earliest stage.
An Effective Classification of Brain Tumor using Deep Learning Technique
Authors:   Professor & Head/ Computer Science and Engineering, Sri Krishna College of technology, Coimbatore, Tamilnadu., Balamurugan* Dr. A. , Devi Dr. S. Siamala ,   Associate Professor in the department of computer science and Engineering. Sri Krishna College of technology, Coimbatore, Tamilnadu., G Rajakumar ,   Department of Computer Science and Engineering., S Sreegandh ,   Department of Computer Science and Engineering., S Vivek ,   Department of Computer Science and Engineering.
Published: May 2020

Health experts have increased taking advantage of the benefits of most modern technologies, thus generating a scalable improvement in the health care area. Because of this, there is a paradigm shift from manual monitoring towards more accurate virtual monitoring with minimum percentage of error. Advances in artificial intelligence (AI) led to exciting solutions with high accuracy for medical imaging technology and is a key method for enhancing future applications. Detection task of brain tumor is difficult in the medical field. Detection of brain tumors manually is time consuming and requires large number of MRI images for cancer diagnosis. So, there is a need for automatic brain tumors detection from Brain MR Images. Deep learning methods can achieve this task. Different deep learning networks can be used for the detection of brain tumors. The proposed method comprises a classification network which classifies the input MR images into 2 classes: one with tumor and the second without tumor. In this work, detection of brain tumor is done via classification by retraining the classifier using the technique known as transfer learning. The obtained result shows that our method works better than the existing methods. The most purpose of this project was to create a deep learning model that will classify if a subject features a growth or not based on MRI scan. I used the VGG-16, Inception v3, and Resnet.
Soil Fertility Analysis and Crop Prediction using Machine Learning
Authors:   Assistant Professor ,Dept. of CSE, BMS Institute of Technology & Management, Yelahanka, Bengaluru, Karnataka,India,., M S* Prof. Muneshwara , Gracias Ms.Abigail Angela ,   UG Student, Dept. of CSE, BMS Institute of Technology & Management, Yelahanka, Bengaluru, Karnataka,India, Neha Ms. C G ,   UG Student, Dept. of CSE, BMS Institute of Technology & Management, Yelahanka, Bengaluru, Karnataka,India., Ms. Preethi  ,   UG Student, Dept. of CSE, BMS Institute of Technology & Management, Yelahanka, Bengaluru, Karnataka,India., S Mr. Akarsh ,   UG Student, Dept. of CSE, BMS Institute of Technology & Management, Yelahanka, Bengaluru, Karnataka,India.
Published: May 2020

Through the exploitation of scientific knowledge and technology, man has made wonderous strides in the field of automation, the focus of which lies in 'Robotics and Machine Learning'. All around us we see machines taking over work with accuracy and ease. Seen as a subset of artificial intelligence, machine learning relies on data, patterns in data and inference to aid technology in thinking for itself. This paper aims to apply the science of machine learning in the field of agriculture, by carrying soil fertility analysis using most accurate algorithm. The fertility of soil plays a principal role in determining the suitability of cultivating a particular crop on a given soil type. Analysis is carried out by the examination of various properties of the soil like the pH value, Electrical Conductivity, Moisture content, Temperature and (N)Nitrogen (P)Phosphorous (K) Potassium levels, followed up by soil type classification. Finally, a recommendation for the most suitable crop is provided in real time.
Improving Network Trust using Blockchain Based Communication Links
Authors:   PG Scholar, Department of Electronics Engineering , G H Raisoni College of Engineering,Nagpur,India, Likhar* Aishwarya , Agrawal Rahul ,   Assistant Professor, Department of Electronics Engineering, G H Raisoni College of Engineering Nagpur,India, Dorle Sanjay ,   Professor, Department of Electronics Engineering, G H Raisoni College of Engineering ,Nagpur,India
Published: May 2020

Wireless networks enable wireless-nodes to develop and broadcast messages in an attempt to reinforce congestion protection and performance. Meanwhile, due to distrust environments, it’s mile tough for the wireless-nodes to assess in reliability of the acquired messages. In this work, we advise the decentralized control machine in Wireless networks situated on the blockchain techniques. During this machine, wireless-nodes must be verifying obtained messages from the neighboring Wi-Fi nodes by using Bayesian Inference Model. On the idea of this validation outcome, Wi-Fi node is going to be generated the rating for every message source of wireless-node. With this ranking uploaded from Wi-Fi nodes, Roadside Units are often calculated the trust cost offsets of worried wireless nodes, p.C. This statistic right into the block. Then, to every of the Roadside Unit are going to be attempt for adding their “blocks” to be consider block chain that's maintain with aid of all Roadside Units. Make the utilization of the joint Proof-of-Work and Proof-of-Stake consensus the system, extra overall fee of the offset (stake) is within a block, more easy the Roadside Unit are often located the nonce for their hash feature (evidence-of-paintings).During this manner, all the Roadside Unit collaboratively preserve an up to the date, dependable, and steady believe blockchain. Clone results can display that the proposed gadget be powerful also a possible in accumulating, computing, and storing agrees with values in Wireless networks.
Application of Machine Learning for Prediction of Lung Cancer using Omics Data
Authors:   Assistant Professor, Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala., Dhillon* Arwinder , Kaur Amrita ,   research scholar in Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala., Singh Ashima ,   Leturer in Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala
Published: May 2020

Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.
An Efficient Text Image Elimination App for Android
Authors:   Assistant Professor, Department of Information Technology, CBIT, Hyderabad, India., Ramalakshmi* Eliganti , Kakarla Hemanth Reddy ,   Student, BE, Information Technology, CBIT, Hyderabad. India, Jella Kranthi ,   Student, BE, Information Technology, CBIT, Hyderabad. India
Published: May 2020

Smartphone sales are at an all-time high with the ever increasing use of smartphones, we have access to data like never before. But with information that vast, we often get suffocated by the images that clutter our devices, making it that much harder to get to the pictures that matter to us. With the power of Machine Learning and image processing, this Text Image Elimination App will free your device from all the clutter and make your life easier. The application removes the images they may not want on their phone. Images containing text will be processed and the text extracted from the images will be compared to a list of texts given by the user and if there is any instance of the text in the image it will automatically be deleted. This paper proposes an efficient android application where the user can optimize the storage space by eliminating the images of his interest based on text matching without directly going in to the directory.
Assessment of Physical and Mechanical Behaviour of Foam Concrete using Bottom Ash and Fly Ash
Authors:   Department of Civil Engineering, Sri Krishna College of Technology, Coimbatore, India., C.V Amritha , I Padmanaban. ,   Department of Civil Engineering, Sri Krishna College of Technology, Coimbatore, India., S. Dharsana ,   Department of Civil Engineering, Sri Krishna College of Technology, Coimbatore, India., K* Balamurali. ,   Department of Civil Engineering, Sri Krishna College of Technology, Coimbatore, India.
Published: May 2020

Foam concrete is a kind of air inflated lightweight concrete, which doesn’t contain coarse aggregate and can be regarded as an air inflated mortar. Foam concrete is made by adding foam into the cement slurry, the main use of foam in the cement mortar is to create air voids which act as a ball bearings inside the concrete mix. Foam is prepared by using foam generator or it can be made by sonication process of hydrogen peroxide. In this research work foam is formed by the sonication process of hydrogen peroxide separately then the foaming agent is diluted with water and air inflated to create the foam. Bottom ash from thermal power plant is collected and added in different proportions in the concrete mix to study the mechanical behaviour of the foam concrete. Mechanical properties such as compressive strength, split-tensile strength, flexural strength and thermal conductivity tests are carried out under controlled environment. The targeted design densities of all the specimens were expected to be 1600 kg/m3. The binder to filler ratio used in this study is 1:1, with the sand being completely replaced with bottom ash and Fly ash.
Effect of Social Media Botnets and their Detection Techniques
Authors:   Associate Professor, University Institute of Computing, Mohali, India., Jain* Dr. Amit , Shukla Dr. Anand Kumar ,   Associate Professor, University Institute of Computing, Mohali, India., Kumar Dr. Raju ,   Associate Professor, University Institute of Computing, Mohali, India.
Published: May 2020

The Online Social Network (ONS) or Social Media have become most popular platform for millions of users for their activities and at the same time it has become favorite place for cyber criminals for their illegal activities, generally known as social botnets, which uses different techniques to spread their information on social media like facebook, twitter, renren, linkedin etc. Several researchers have tried to detect several social botnets with different detection techniques. To avoid the detection, social botnets are now using advanced command & control (C&C) communication channels like hash tags, fraud click, friend requests, images, videos etc. Image Steganography techniques are now widely being used to carry out attacks. In this paper, the primary discussion is related to effects of social media botnets along with the different techniques for botnet detection. It also, explores the use of machine learning mechanism, thereby detecting the intrusions in stegano images. Thus, an effort has been made to localize the factors that have a major role in social intervention as a whole.
Classification of Soil and Prediction of Crop
Authors:   Department of Information Science and Enginnering, V.P.DR P.G.Halakatti College of Engineering and Technology Vijayapura, India., Honawad* Shivakumar K. , Chinchalli Santosh S. ,   Department of Information Science and Enginnering, V.P.DR P.G.Halakatti College of Engineering and Technology Vijayapura, India.
Published: May 2020

Agriculture is a major backbone for most families. As we move from one location to other the soil is different. The yield from crop is not as much as accepted. The soil classification and crop prediction is done manually, so research in this field is at most importance. The Digitization technique has employed in this paper to overcome the manual task. Hence SCP algorithm has been implemented. The algorithm classifies images and provide suitable crop for classified soil. So this work can be used in agriculture field.
Performance Analysis of Classification Algorithms for Fault Diagnosis in Rotating Machines
Authors:   CSE, BIT, Durg, India., Sar* Sumit Kumar , Kumar Ramesh ,   CSE, BIT, Durg, India.
Published: May 2020

Classification of any given vibration signal as healthy or faulty can be done by employing classification algorithms available to us. Identification of a fitting classification algorithm is a task that should be done at the time of identification of the problem statement itself, such that required changes can be done in it if the need be. Hilbert Huang Transform (HHT) empowered Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to obtain the most significant features of the vibration signals of both healthy and faulty rotating machines in the time and frequency domain, namely RMS velocity, Kurtosis, and Crest Factor (RKC). They were then fed to classification algorithms to classify the machines as healthy or faulty. Five machine learning techniques such as Probabilistic Neural Network (PNN), decision tree (DT), k- nearest neighbour (KNN), and Radial Basis Network (RBN) are utilized as classification algorithms. Decision Tree algorithm was found to be the optimal classification technique; overfitting was found to be a notable issue. To improve prediction, the decision tree algorithm was parallelly ensembled into Random Forest using the Bootstrap Aggregation method.
The Dominance of Proton/Neutron Evaporation Exit-Channel in a Fusion-Evaporation Reaction: The Effect of Target Nuclear Ratio (N/Z)
Authors:   Department of Physics, Bethune College, Kolkata, India., Ganguly* Dr. Sourav , Choudhury* Mr Tirthankar ,   Department of Physics, Haldia Government College, Haldia, India.
Published: May 2020

Cross section of different evaporation residue have been calculated in 112Sn+16O (Neuron/Proton (N/Z) of the 112Sn target is 1.24) and124Sn+16O reaction (N/Z of the 124Sn is 1.48) with beam energy of 80 MeV using statistical model calculation code PACE4. These calculations predicts that the proton emission channels are predicted to be dominant when the N/Z ratio is small (i.e in the first reaction) whereas the neutron emission outgoing channels dominant in the second reaction when N/Z is large. Experimental phenomenon also revealed the fact that in order to populate the proton or neutron reach nucleus we have to choose the target material accordingly.
Accuracy Assessment & Classification of Keratosis Skin Lesion Images using Feature Extraction & Classification Algorithms-LBP, LDP& HOG
Authors:   Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India., Rao* Manjunath , Fernandez Calvin Joshua ,   Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India., K Sreekumar ,   Asst. Professor of Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
Published: May 2020

There are hundreds of human-affected skin diseases. The most severe skin disorders may have identical symptoms, so recognizing the distinctions between them is crucial. People should work closely with a dermatologist to identify and manage every skin disorder and insure it does not impact their lifestyle.Actinic keratosis (AK), that is also classified as solar or senile keratosis; is a pre-malignant crusty, thick skin area. It is a disorder of epidermal keratinocytes, induced by UV radiation upon the skin. While pre-cancerous in nature, they can develop into a form of skin cancer called carcinoma if left unaddressed. The other type of keratosis dealt within this paper is seborrheic keratosis, which are brown or black, thick, wart-like, waxy oval-shaped, slightly raised skin surfaces. The growths aren't damaging. Nevertheless, in some instances it can be impossible to differentiate a seborrheic keratosis from melanoma, which is a very dangerous form of skin cancer.Nevus (or moles) skin lesions are ones which are benign, where it may very rarely turn into melanoma skin cancer. In this article, along with techniques for extracting features (LDP [Local Directional Patterns], LBP [Local Binary Patterns] and HOG [Histogram of Oriented Gradients]),we have used an SVM classifier for the classification of Keratosis and also nevus skin photos. The LBP, LDP and HOG are means to extract features; these images are subsequently used for identification of derived features from these methods or algorithms and classified by the SVM (Support Vector Machine) classifier. For many of the classifications of keratosis and nevus skin images using these algorithms, we have obtained accuracy nearly above 80 %, whereby the LBP system together with the SVM classifier was the most powerful attribute extraction tool of the three with their polynomial kernel type. Using this algorithm-classifier,the main AK and nevus skin lesion images can be detected and diagnosed by the doctors in its early stage itself,thus helping save lives.
Restaurants Rating Prediction using Machine Learning Algorithms
Authors:   PG Student, Department of Computer Science, GITAM (Deemed to be University), Visakhapatnam, Malik* Vicky , Vagolu S.Prasad Babu ,   Assistant Professor, Department of Computer Science, GITAM (Deemed to be University), Visakhapatnam, Chandolu Sunil ,   Assistant Professor, Department of Computer Science, GITAM (Deemed to be University), Visakhapatnam
Published: May 2020

Restaurant Rating has become the most commonly used parameter for judging a restaurant for any individual. A lot of research has been done on different restaurants and the quality of food it serves. Rating of a restaurant depends on factors like reviews, area situated, average cost for two people, votes, cuisines and the type of restaurant. The project aim is to find out the relationship between the dependent and independent variable. Proposed project is a Machine Learning Regression problem which uses Restaurant Rating dataset. Based on various attributes like the food, quality, prize ambience of the restaurant it predicts the Restaurant Rating.
Analysis of Bit Error Rate on M-ary QAM over Gaussian and Rayleigh Fading Channel
Authors:   ECE Department, K.S. Institute of Technology (affiliated to VTU), Bangalore, India., Sangeetha* V. , Sudha Dr.P.N. ,   Prof &Head of ECE Department, K.S. Institute of Technology (affiliated to VTU), Bangalore, India.
Published: May 2020

Transmission of signal over long distance through the channel will result in poor signal quality reception at the receiver. The Signal quality is affected by means of fading and it can be minimized by using effective modulation techniques. M-ary QAM is one of the effective modulation techniques as it has higher efficiency and effective form of modulation for data. M-QAM is a modulation where data bit selects M combinations of amplitude and phase shifts that are applied to carrier. The analysis carried out based on Bit Error Rate on various M -Quadrature Amplitude Modulation schemes like 16 -QAM, 64- QAM and 256- QAM over Gaussian and Rayleigh Fading channel. The input data entered into QAM modulator then transmits over Gaussian channel and the QAM demodulator is performed at the receiver. The same process repeated over Rayleigh fading channel for M-QAM. Rayleigh fading is multipath fading channel will vary randomly according to the Rayleigh distribution. The MATLAB simulation is carried out to get experimental results on M-QAM and compared. The analysis shows that improved Bit Error Rate in 16-QAM over Gaussian and Rayleigh fading channel.
Emotion Detection Analysis using EEG and Physiological Signals for Hybrid Systems
Authors:   Research Scholar Master of Computer Applications (MCA) Department of Computer Science CHRIST (Deemed to be University), Bangalore – 560029, Karnataka, India, Rupani* Namrata D , Mary R. Roseline ,   Assistant Professor Department of Computer Science CHRIST (Deemed to be University), Bangalore – 560029, Karnataka, India
Published: May 2020

Emotions are an inevitable and integral part of human existence. They form the basis of decisions taken by individuals and the way they perceive their surroundings. Method of articulation of emotions have changed with the increment in dependency between people and innovation. Now the need to recognize emotions has increased with the increasing role of human-Computer Interface (HCI) technology. There are many ways to record and identify human’s emotion using different neurophysiological measurements/ technologies like GSR(Galvanic Skin Response), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper, the focus is on emotion detection using EEG signals and other physiological signals and further analyzing them. There exist various machine learning techniques that have been used to pre-process and classify EEG data, have been reviewed in the paper. The analysis involves major aspects of the emotion recognition process like feature extraction, classification and comparison of the approaches. Different supervised machine learning algorithms have been applied to classify the EEG data. This paper focuses on comprehensive analysis of existing systems and based on the result propose the techniques which when applied will reap high-quality results.
Machine Learning-Based Technique for Image Classification and Identification of Similarity for Commonness of Source
Authors:   Pursuing B.E, Computer Science and Engineering, SCSVMV Deemed, University, Kanchipuram, Tamil Nadu, India., Sruthi* V. , Narayanan S.Lakshmi ,   Pursuing B.E, Computer Science and Engineering, SCSVMV Deemed, University, Kanchipuram, Tamil Nadu, India.
Published: May 2020

In India Every year RBI (Reserve bank of India) faces the issue of fake currency. Fake Currency has consistently been an issue that has made a lot of chaos in the market. The expanding mechanical progressions have made the opportunities for making progressively fake currency which is circled in the market which decreases the general economy of the nation. There are machines present at banks and other business regions to check the validness of the monetary forms. Be that as it may, a typical man doesn't approach such frameworks and henceforth a requirement for a product to distinguish counterfeit cash emerges, which can be utilized by average folks. This proposed framework utilizes Image Processing to identify whether the currency is real or fake. The framework is structured utilizing Python programming language and OpenCV. It comprises of the means, for example, grayscale detection, edge detection, Highlight Extraction, and so forth which are performed utilizing reasonable strategies. And which will be further implemented in the Framework for Classification and Identification of Similarity for Commonness of Source
Segmentation of Brain Tumor using Glcm and Discrete Wavelet Transform
Authors:   CSE, Ansal University, Gurugram, Haryana, India., Jijja* Alpana , Rai Dr. Dinesh ,   CSE, Ansal University, Gurugram, Haryana, India.
Published: May 2020

To identify brain tumors at an early stage is a challenging task. The brain tumor is usually diagnosed with Magnetic Resonance Imaging (MRI). When MRI spectacles a tumor in the brain, the most common way of determining the type of brain tumor after a biopsy or surgery is to look at the results of a tissue sample. In this research to detect brain tumors faster and accurately the feature extraction techniques are used to segment the tumor affected area. One of such very effective technique of feature extraction measure is the Grayscale Co-occurrence Matrix (GLCM). This research focuses on the GLCM and Discrete Wavelet Transformation (DWT) technique to detect and label the tumor from an image based on the textures and categorizing it according to a tumor or non-tumor category. The convolutional neural network (CNN) uses these features to improve the accuracy to 91%.
An Effective Integration of Domain Knowledge in-to Deep Neural Networks
Authors:   Pursuing Bachelor’s Degree, Sri Krishna College of Tech-nology, Coimbatore, TamilNadu, India., S* Arunima , S Kiruthika ,   Assistant Professor, Department of Computer at Sri Krish-na College of Technology, Coimbatore, Tamil Nadu, India., Raj M. Aswin ,   Pursuing Bachelor’s Degree,. Sri Krishna College of Technology, Coimbatore, TamilNadu, India., Kumar K.Kavin ,   Pursuing Bachelor’s Degree,. Sri Krishna College of Technology, Coimbatore, TamilNadu, India., Prakash S.Surya ,   Pursuing Bachelor’s Degree,. Sri Krishna College of Technology, Coimbatore, TamilNadu, India.
Published: May 2020

Machine learning in recent years has become an integral part of our day to day life and the ease of use has im-proved a lot in the past decade.There are various ways to make the model to work in smaller devices.A modest method to advance any machine learning algorithm to work in smaller devices is to provide the output of large complex models as input to smaller models which can be easily deployed into mobile phones .We provided a framework where the large models can even learn the domain knowledge which is integrated as first-order logic rules and explicitly includes that knowledge into the smaller model by simultaneously training of both the models.This can be achieved by transfer learning where the knowledge learned by one model can be used to teach the other model.Domain knowledge integra-tion is the most critical part here and it can be done by using some of the constraint principles where the scope of the data is reduced based upon the constraints mentioned. One of the best representation of domain knowledge is logic rules where the knowledge is encoded as predicates.This framework provides a way to integrate human knowledge into deep neural networks that can be easily deployed into any devices.
Feature Fusion: An Application To Biomedical Signal Classification
Authors:   Department of Computer Science, Pub Kamrup College, Assam, India, Sarmah* Arup , Lahkar Rahul ,   Department of Computer Science, Pub Kamrup College, Assam, India, Kalita Sanjib ,   department of Computer Science, Gauhati University, Kamrup, India, Choudhury B K Dev ,   Pub Kamrup College, Assam, India.
Published: May 2020

Development of a feasible support system for automating staging of neural disorder based on Electroencephalogram (EEG) is essential to speed-up diagnosis process by improving the burden of the clinician of analyzing large volume data and to accelerate large scale research. In this work Discrete wavelet transform (DWT) has been applied to extract statistically independent features and fused the features for effective classification of various EEG signal. The aim of this paper is to present a comparative study of two feature fusion approaches namely Canonical Correlation Analysis (CCA) and Discriminant Correlation Analysis (DCA). Further, our proposed method can be extended to develop a graphical user interface and promote real time implementation.
Correlation Between Mentors’ Communication in E-Mentoring, Mentees’ Self-Efficacy and Mentees’ Academic Performance: Evidence from a Malaysian Public Research University
Authors:   Faculty of Economics & Management, Universiti Kebangsaan Malaysia, Selangor, Malaysia., Ismail* Azman , Arshad Mohd Mursyid ,   Faculty of Educational Studies, Universiti Putra Malaysia, Selangor, Malaysia., Samsudin Asyakireen ,   Faculty of Economics & Management, Universiti Kebangsaan Malaysia, Selangor, Malaysia., Mohamad Nur Izzaty ,   Institute of Islam Hadhari, Universiti Kebangsaan Malaysia, Selangor, Malaysia., Omar Najihah ,   Faculty of Economics & Management, Universiti Kebangsaan Malaysia, Selangor, Malaysia.
Published: May 2020

E-mentoring is often viewed as an informal teaching and learning method in excellent higher education institutions. Recent studies highlight that well-designed e-mentoring programs will not be able to support their objectives if mentors do not have capabilities to communicate effectively in the mentoring system. Even though this relationship has widely been examined, the role of mentors’ communication in e-mentoring as a significant determinant is not adequately discussed in the higher education institution literature. Thus, this study was done to assess the correlation between mentors’ communication in e-mentoring, mentees’ self-efficacy and mentees’ academic performance. A survey method was utilized to collect data from students at a public research university in Malaysia. The outcomes of the Statistical Package for Social Science displayed that the competency of mentors to openly deliver mentoring information through technology media had strongly invoked mentees’ self-efficacy. As a result, this situation could lead to an enhanced mentees’ academic performance in the examined higher education institution.
Особенности внедрения процедур, основанных на принципах ХАССП, для обогащенных мучных кондитерских изделий
Authors: Резниченко Ирина Юрьевна , Чистяков Андрей Михайлович , Устинова Юлия Владиславовна
Published: May 2020

В статье представлены результаты разработки системы менеджмента безопасности пищевой продукции в соответствии с ГОСТ Р ИСО 22000-2019 «Системы менеджмента безопасности пищевой продукции. Требования к организациям, участвующим в цепи создания пищевой продукции» на кондитерском предприятии ООО «Кондитер» для обогащенного овсяного печенья. Построение системы безопасности пищевой продукции актуально в отношении любого предприятия, имеющего непосредственное или косвенное отношение к цепи производства продуктов питания, особенно это важно для предприятий, выпускающих продукцию обогащенную, специализированного и функционального назначения. На основании анализа опасных факторов и применения алгоритма определения критических контрольных точек выделены рациональные для управления и эффективного контроля объективные контрольные точки, оказывающие значительное влияние на качество, безопасность и функциональную направленность обогащенного изделия. Предложены процедуры при внедрении системы менеджмента качества и безопасности для обогащенных мучных кондитерских изделий, реализация которых имеет существенное значение в обеспечении стабильности качества и конкурентоспособности продукции на потребительском рынке. Разработанная система поможет руководству ООО «Кондитер» организовать устойчивую систему идентификации, оценки и контроля опасностей, выявить потенциальные риски, угрожающих безопасности пищевых продуктов на протяжении жизненного цикла продукции; использовать систему мониторинга и измерений в критических пределах.
Разработка технологии и оценка эффективности нового продукта – функционального безглютенового кекса
Authors: Жаркова Ирина Михайловна , Сафонова Юлия Александровна , Густинович Василий Григорьевич , Ильева Татьяна Львовна
Published: May 2020

Для больных, страдающих целиакией или другими формами непереносимости глютена, основным условием при составлении рациона питания является отказ от изделий, содержащих в своем составе продукты переработки зерна пшеницы, ржи, ячменя, то есть строгое соблюдение безглютеновой диеты. Однако, отмечается ограниченность химического состава безглютеновых продуктов, что может отрицательно сказываться на здоровье и самочувствии больных, соблюдающих такой режим питания в течение длительного времени. Поэтому перед исследователями стоит задача создания новых безглютеновых изделий, обогащенных необходимыми веществами: растительной клетчаткой, витаминами, кальцием, магнием, железом и так далее. С этой целью при разработке рецептуры такой категории мучных изделий используют нетрадиционное сырье. В работе представлены результаты исследования по отработке технологии производства безглютеновго кекса с применением амарантовой муки, муки из клубней чуфы, морковного и яблочного порошков, а также смеси стевиозида и изомальта. При проектировании рецептуры нового изделия было использовано специализированное программное обеспечение, которое на основе составленной математической модели позволило определить оптимальный рецептурный состав моделируемого продукта с необходимым соотношением ингредиентов, согласно заданным ограничениям. Были получены шесть вариантов рецептур безглютенового кекса. Оценка совокупности органолептических и физико-химических показателей позволила выбрать наилучшую рецептуру, сгенерерированную программой. Белок безглютенового кекса содержит все незаменимые аминокислоты. Причем получены экспериментальные данные, подтверждающие высокую степень сбалансированности аминокислотного состава белков безглютенового кекса. Полученное изделие можно отнести к группе функциональных пищевых продуктов по содержанию фосфора, магния, селена, а для отдельных групп потребителей – также по содержанию железа, кальция, калия. По показателям безопасности разработанный кекс соответствует требованиям ТР ТС 021. Возможность включения его в рацион питания больных с симптомами непереносимости глютена подтверждена с участием добровольцев. Разработана функциональная и аппаратурно-технологическая схемы производства безглютенового кекса.
Местная устойчивость стальных сетчатых куполов с шарнирным соединением стержней в узлах
Authors: Глушко К. К. , Глушко К. А.
Published: May 2020

Рассмотрена задача местной устойчивости стальных сетчатых куполов с шарнирным соединением стержней в узлах. Определены графические зависимости изменения критической узловой нагрузки в зависимости от податливости узлов. Определены границы устойчивости формы стержневых многогранников, образующих сетчатые купола, и границы потери устойчивости отдельных стержней.
Использование модели трехслойного основания в расчетах шарнирно-соединенных дорожных плит
Authors: Козунова О. В.
Published: May 2020

В работе рассматривается задача о расчете шарнирно – соединенных в отдельных точках прямоугольных дорожных плит на трехслойном упругом основании на произвольную внешнюю нагрузку. Расчет выполняется смешанным методом строительной механики с использованием способа Б. Н. Жемочкина. Смежные плиты соединяются цилиндрическими шарнирами в двух точках. Реализация предлагаемого подхода выполнена для семи шарнирно – соединенных прямоугольных дорожных плит под действием системы сосредоточенных сил, каждая из которых приложена в середине плиты. В численных результатах приводятся линейные перемещения плит, реактивные давления на контакте плит с упругим основанием, поперечные силы в соединительных шарнирах. Предлагаемый в работе новый универсальный подход для статического расчета шарнирно-соединенных плит на упругом основании легко обобщается на плиты конечной жесткости и иные модели упругого основания.
Pengaruh Pembelajaran Kontekstual Terhadap Keterampilan Berpikir Rasional Siswa Pada Subkonsep Pencemaran Air
Authors: Galuh Bayu Purnama
Published: May 2020

Penelitian ini bertujuan untuk memperoleh informasi mengenai penggunaan model pembelajaran kontekstual terhadap peningkatan keterampilan berpikir rasional  siswa. Metode yang digunakan dalam penelitian ini adalah  metode weak eksperiment, dengan mengambil satu kelas yaitu kelas X MIPA 3 sebagai kelas penelitian yang di ambil di SMA Negeri 7 Pandeglang. Data dari penelitian diperoleh dengan menggunakan instrumen berbentuk pilihan ganda sebanyak 20 soal yang diberikan pada waktu tes awal (pre test) sebelum proses pembelajaran diberikan dan tes akhir (post tes) setelah proses pembelajaran. Keterampilan berpikir rasional yang digunakan dalam penelitian ini mencakup 7 indikator yaitu mengingat, mengelompokan, menganalisis, mengevaluasi, membandingkan, meramalkan, dan menyimpulkan. Diperoleh rata-rata pre test keterampilan berpikir rasional sebesar 64,3 dan rata-rata post test keterampilan  berpikir rasional sebesar 79,86. Dari ke tujuh indikator kemampuan berpikir rasional yang diukur, peningkatan indikator mengingat menempati urutan paling tinggi 86,40 dan peningkatan indikator terendah adalah indikator mengenalsis sebesar 73,40. Setelah diketahui hasil pre test dan post test peneliti melanjutkannya dengan uji t berpasangan, dan diperoleh hasil uji t berpasangan yang signifikan karena thitung >ttabel sebesar 7,74>2,73. Hasil penelitian ini menunjukkan bahwa proses pembelajaran menggunakan kontekstual  dapat meningkatkan keterampilan berpikir rasional siswa.