bluuebunny/crossref_metadata_2025 huggingface dataset:
Kimyasal, Biyolojik, Radyasyon ve Nükleer (KBRN) Olaylarda Psikososyal Bakım
Authors: DEMİRALP Nuray , DEMİRALP Kemal , ÜTÜK Ayşe , ÜTÜK Ömer Faruk
Published: May 2020

Afetler ani, çoğunlukla nerede ve ne zaman olacağı tahmin edilemeyen, çok sayıda insanın etkilendiği, ekonomik, çevresel kayıpları, yıkıcı etkileriyle toplumun bununla başa çıkamaması ile karakterize bir durumdur. Bireyleri (örneğin çocuklar, ebeveynler, ilk müdahale edenler), acil yardım, sivil toplum kuruluşları (örneğin tıbbi acil durum ekipleri, halk sağlığı kuruluşları, itfaiye ekipleri) ve toplumu etkilemektedir. Afetler verdikleri zararlar nedeniyle bireyler ve toplumların yaşamlarında derin izler bırakan olaylardır. Yaşanan afet ve acil durumun bireyler ve toplumlar üzerindeki yıkıcı etkilerini en aza indirmek için en fazla ihtiyaç duyulan hizmet alanı psikososyal destektir. Psikososyal destek; hayatın normale dönüştürülmesi için gösterilen çabaların psikolojik ve sosyal açıdan değerlendirilmesine olanak sağlayan, bütüncül bir bakış açısını ve çok disiplinli bir müdahaleyi gerektiren hizmetlerdir. Kimyasal, biyolojik, radyolojik veya nükleer (KBRN) olaylar söz konusu olduğunda felaketler veya krizler bağlamında aynı şekilde psikososyal bakım müdahalelerine ihtiyaç vardır. Bununla birlikte, KBRN olayları acil durum planlaması ve afet hazırlığında ayrı bir tema oluşturduğundan psikososyal bakım üzerindeki etkilerini sistematik olarak araştırmak önemlidir. Bu çalışmada KBRN olaylarında psikososyal bakımda genel kurallar üzerine durulmuş ve KBRN olaylarında afet ve acil durum olaylarından farklı psikososyal durumlar açıklanmaya çalışılmıştır. İncelenen literatür çalışmalarında; KBRN ile ilgili psikososyal bakım ile ilgili hususlar konusunda çoğu çalışmada genel olarak varılan sonuç; genel afet sonrası psikososyal bakım kılavuzlarına benzer veya tamamlayıcı niteliktedir. Dikkate değer farklar ise iletişim, KBRN ajanlarına maruz kalma, kirlenme (kontaminasyon) ve KBRN olaylarına yönelik özel hazırlık gereksinimlerine vurgu yapılmaktadır. Ülkemizde ise KBRN olayların psikososyal etkileri ve psikososyal bakım ile ilgili çalışmalara çok az rastlanmaktadır. Bu alanla ilgili özellikle saha çalışmaları yapılarak literatüre büyük katkı sağlayacağı düşünülmektedir.
Association Rule Mining on Spambase Dataset using Tanagra
Authors:   Department of Computer Science, Himachal Pradesh University, Summerhill, Shimla -5, Himachal Pradesh, India., Devi* Pushpa , Bhardwaj Vikrant Singh ,   Department of Computer Science, Himachal Pradesh University, Summerhill, Shimla -5, Himachal Pradesh, India., Bansal Dr. Kishori Lal ,   Professor Department of Computer Science, Himachal Pradesh University, Summerhill, Shimla -5, Himachal Pradesh, India.
Published: May 2020

There is huge amount of data being generated every minute on internet. This data is of no use until we cannot extract useful information from it. Data mining is the process of extracting useful information or knowledge from this huge amount of data that can be further used for various purposes. Discovering Association rules is one of the most important tasks among all other data mining tasks. Association rules contain the rules in the form of IF then THAN form. The leftmost part of the rule i.e. IF is called as the Antecedent which defines the condition and the rightmost part i.e. ELSE is called as the Consequent which defines the result. In this paper, we present the overview and comparison of Apriori, Apriori PT and Frequent Itemsets algorithm of association component in Tanagra Tool. We analyzed the performance based on the execution time and memory used for different number of instances, support and Rule Length in Spambase Dataset. The results show that when we increase the support value the Apriori PT takes the less execution time and Apriori takes less memory space. When numbers of instances are reduced Frequent Itemsets outperforms well both in case of memory and execution time. When rule length is increased the Apriori algorithm performs better than Apriori PT and Frequent Itemsets.
An Efficient Algorithm for Text Mining in Business Intelligence using Machine Learning
Authors:   Amity School of Engineering and Technology,Amity University Madhya Pradesh, Maharajpura Dang,Gwalior(MP)-474005,India., Mishra* Devendra Kumar , Upadhyay Prof.( Dr.) Arvind Kumar ,   Amity School of Engineering and Technology,Amity University Madhya Pradesh, Maharajpura Dang,Gwalior(MP)-474005,India., Sharma Dr. Sanjiv ,   Madhav Institute of Technology and Science,Gwalior(MP) ,India.
Published: May 2020

Data plays an important role in success of any organization, so organizations required more data to make decision for their planning to improvement. The data that are generating for any organization, in which 80 to 90 percent data belongs to unstructured data type.Text mining is the process that indicate retrieve appealing and unknown information from unstructured text. Social network sites also generate huge amounts of data,with the help of these data people’s behavior and thought easily determine but analysis of these data is a difficult task. This paper proposed an efficient approach for text mining using machine learning.
The Deepfake Challenges and Deepfake Video Detection
Authors:   Lecturer at Wolkite University, Wolkite Ethiopia., Wubet* Worku Muluye
Published: May 2020

Deepfake is a combination of fake and deep-learning technology. Deep learning is the function of artificial intelligence that can be used to create and detect deepfakes. Deepfakes are created using generative adversarial networks, in which two machine learning models exit. One model trains on a dataset and then creates the deepfakes, and the other model tries to detect the deepfakes. The forger creates deepfakes until the other model can't detect the deepfakes. Deepfakes creating fake videos, images, news, and terrorist events. When deepfake videos, and images increase on social media people will ignore to trust the truth. Deepfakes are increasingly affecting individuals, communities, organizations, security, religions, and democracy. This paper aims to investigate deepfake challenges, and to detect deepfake videos by using eye blinking. Deepfake detections are methods to detect real or deepfake images and videos on social media. Deepfake detection techniques are needed original and fake images or video datasets to train the detection models. In this study, first discussed deepfake technology and its challenges, then identified available video datasets. Following, convolutional neural networks to classify the eye states and long short term memory for sequence learning has been used. Furthermore, the eye aspect ratio was used to calculate the height and width of open and closed eyes and to detect the blinking intervals. The model trained on UADFV dataset to detect fake and real video by using eye blinking and detects 18.4 eye blinks per minute on the real videos and 4.28 eye blinks per minute on fake videos. The overall detection accuracy on real and fake videos was 93.23% and 98.30% respectively. In the future research and development needs more scalable, accurate, reliable and cross-platform deepfake detection techniques.
Programmable FFT Processor using Dual RAM and ROM Technologies for Future 5G Communications
Authors:   M.Tech Student Department of ECE,MVGR College of Engineering ,Vizianagaram., Hemalakshmi* Ch. , Keerthi L. ,   M.Tech student Department of ECE,MVGR College of Engineering, Vizianagaram., Vali Shaik Mastan ,   Professor, Departement of ECE,MVGR College of Engineering, Vizianagaram.
Published: May 2020

The high-throughput programmable Fast Fourier transform processor supports the usage of 2-stream 1024/2048/4096-point Fast Fourier Transforms and 1-to 4-stream 64/128-point Fast Fourier Transform for 4G,wireless local networks and for 5G.The proposed architecture which was designed is a well-intentionedfour-bank single-port SRAM which is being working in four-word data width, the design which is proposed gives us sixteen memory pathways . where the data is accessed up to this extent where it can be used in upcoming 5G. The radix-16 butterfly process element comprises of 2 cascaded parallel, pipelined radix-4 butterfly units which is specified. The projected memory-addressing methodology will effectively wear down single-port, merged-bank memory with high-radix process components. Comparing with typical memory based Fast Fourier Transform styles, the derived design has higher performance in expressions of area and power consumption. The architecture which is projected occupies the tiniest area of around1.21mm2.The processor supports 1966MS/s 4096-point FFT and frequency of 1GHz.The Electronic design automation synthesis results show the power consumption is 32.16mW.The SQNR performance analysis is 42.14 dB.
Real Time Gender and Age Prediction using Deep Learning Techniques
Authors:   Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India., Selvy* Dr.P.Tamije , Sujith M. ,   Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India., Sanjay V. ,   Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India., Sreeram G. ,   Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India., Anitha M. ,   Full-Time Ph.D Scholar, in Sri Krishna College of Technology in the Department of Computer Science and Engineering.
Published: May 2020

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.
Application of Failure Mode Effect Analysis for Improved Scheduling in Maintenance of Machines
Authors:   Associate Professor, Vaish College of Engineering, Rohtak, India, Gupta* Dr. Umesh , Bansal Dr. Ankit ,   Associate Professor, Vaish College of Engineering, Rohtak, India, Singh Sandeep ,   Assistant Professor, Vaish College of Engineering, Rohtak, India
Published: May 2020

Maintenance of machines is crucial measures in order to have stable and improved work flow. Any kind of failure might result in complete failure of the machine. Hence it becomes essential to identify the vulnerable failures that might occur in the components of any machine. The present work is carried out in order to improve the scheduling in maintenance of a lathe machine. Different components of the machine are studied in this research. “Failure mean effective analysis (FMEA)” method is applied to identify the failures associated with the components of the machine. Risk priority number is calculated based on which the components are provided with ranks. The rank signifies the flow of maintenance for all the components. The results reveal that the flexure bearing needs the least maintenance as it has the highest rank.
Property Registration and Ownership Transfer using Blockchain
Authors:   Computer Engineering Department, Marathwada Mitra Mandal’s college of Engineering, Pune, India., Pawar* Reshma Ravindra , Chillarge Geetha.R. ,   Computer Engineering department, Marathwada Mitra Mandal’s college of Engineering, Pune, India.
Published: May 2020

The high-value property like Land, Home, related to real estate it is essential to have exact records that recognize the present proprietor and give evidence that he is surely the proprietor. Such a record can be utilized to protect the owner’s privileges, prevent sale fraud and to make sure that the ownership is correctly transferred to a new owner after sale. Thus it is essential to maintain correct and complete information and prevent illegal or unjustified, fraudulent changes. Many efforts have been taken already for providing data security to sensitive information. Blockchain is the technology that gives high security to the data. Blockchain technology can store an immutable history of transactional records, so no one can ever doubt the authenticity; records are permanently linked to the system so no one can ever interfere with a record of their own. This paper gives a comprehensive system on blockchain technology as it can not only be used in financial assets but anything which has some value.
Risk Analysis & Risk Theories implementation in Hazard Identification & Risk Assessment of Aluminum Industry
Authors:   Subhransu Sekhar Bal , M.tech in Industrial Safety Engineering,Mechanical Department , Bhilai Institute Of Technology,(Affilated to CSVTU C.G,Approved by AICTE,NewDelhi ) City,Raipur Country.India, Sekhar Bal* Subhransu , Gupta Jaykant ,   Asst. Professor ,Mechanical Department , Bhilai Institute Of Technology,(Affilated to CSVTU C.G,Approved by AICTE,NewDelhi ), Raipur, India.
Published: May 2020

Risk Analysis is being done to identify and manage the major / potential problems in the industry . Risk Analysis some times may be more complicated .For doing the risk assessment , risk analysis has to be done . Risk assessment is the technique to analyse the actual level of risk and its consequences associated with a particular activity. In this method one activity is to be taken and that to be discussed with competent risk assessment team and historical data of same is to be analysed to find out the actual consequences of a particular hazardous activity. All the findings should be recorded separately for different activities. Risk Analysis & Risk assessment of a particular unit of the aluminium refinery will be taken in to consideration for the implementation of this technique . By using this technique many hazards can be controlled and the risk of accidents can be reduced in the industries by the proper implementation of Risk Analysis and risk theories. It will be helpful for the Safety professionals to identify the hazards and risks associated with those hazards and successful elimination of the hazard’s or the reduction of the risks by proper implementation of Risk Analysis and Risk Theories .
“Designing and Simulation using Software’s PV SYST & HELIOSCOPE for Mitigating Challenges of Grid Connected PV Based Solar Plant”
Authors:   Power Electronics and Drives, AISSMS’s IOIT, Pune, India, Rohankar* Nilesh B. , C.J Jithin ,   Power Electronics and Drives, AISSMS’s IOIT, Pune, India, Bakre Shashikant Madhukar ,   Professor in Electrical Engineering Department at AISSMS’s IOIT, Pune. India, Shelar Sachin V. ,   Assistant Professor in the Department of Electrical Engineering at AISSMS’s IOIT, Pune. India, Shiralkar Ashpana ,   Head of Department, Electrical Engineering, AISSMS’s IOIT, Pune, India
Published: May 2020

India is having large potential of renewable energy for which Govt of India has a plan to develop 100GW solar generation. Out of which commissioning of 60 GW Distributed RE generation has been the beginning of an era of de-licensed generation. Paper emphasis on the need for active involvement of distribution companies (DISCOMS) for strong scalability of pilot projects concept under clustered solar agriculture feeder in rural areas and its power evacuation with high degree of performance indicators. In this paper we have discussed various issues regarding grid connected ground mounted solar plant with its energy generation, solar insolation, project sizing/designing tools like PV SYST & HELIOSCOPE software with simulation analysis. Paper covers analysis of above software results for critically study annual energy generation & to mitigate its challenges.
The Extensive Interpretation of Ethical Hacking
Authors:   CSE Department, GITAM School of Technology, Bangalore, India., D M* Deepak , G M Kavyashree ,   CSE Department, Bangalore, India.., G Asha ,   CSE Department, GITAM School of Technology, Bangalore, India., D Venugopal ,   CSE Department, GITAM School of Technology, Bangalore, India., K B Rekha ,   CSE Department, GITAM School of Technology, Bangalore, India.
Published: May 2020

Dangerous development of the Internet has introduced diverse beneficial matters: digital enterprise, fundamental get admission to tremendous shops of reference fabric, community organized figuring, email, and new streets for advancing and information scattering, to provide some fashions. Moreover with maximum imaginative advances, there may be also a blurred aspect: crook builders. An moral software program engineer is a PC and framework ace who ambushes a safety structure to support its proprietors, searching for vulnerabilities that a threatening developer could abuse. Governments, associations, and private occupants round the world are irritating to be a some segment of this indignant, anyway they're on side about the likelihood that that some software engineer will destroy into their Web server and override their logo with sex diversion, read their email, take their charge card variety from an on line shopping internet site, or installation programming in an effort to stealthily transmit their affiliation's special bits of information to the open Internet. There are numerous techniques used to hack the records. This paper researches the ethics at the back of systems of appropriate hacking and whether there are issues that lie with this new area of labour.
Stock Prices Prediction with Recurrent Neural Networks
Authors:   Department of Management Studies, Christ(Deemed to be University), Bangalore, India., Raju* Middi Appala , Middi Venkata Sai Rishita ,   Department of Electrical and computer ENgineering, Brown University, Providence, India.
Published: May 2020

Data and Information is the base for making investment choices. Stock market is typically a place where shares of certain companies trying to raise their capital, are traded. With the availability of large amount of data and refinement methods, investors nowadays, are able to make rational investment decisions. Advancement in computational intelligence, use of AI in the form of Neural Networks has created a new basis for predicting stock prices. In this work, we have employed Recurrent Neural Networks to implement time series prediction. The Long Term Short Memory Architecture has been used as the network architecture to perform prediction on Apple Stock Prices. The implementation is done on Keras platform.
Design and Fabrication of Semi Automatic Coconut Copra Electric Dryer
Authors:   Department of Mechanical Engineering, Periyar Maniammai Institute of Science and Technology, Vallam, Tamilnadu, India, Thiyagarajan* R. , Srinivasan P. ,   Department of Mechanical Engineering, Periyar Maniammai Institute of Science and Technology, Vallam, Tamilnadu, India, Aneesh T. ,   Department of Mechanical Engineering, Periyar Maniammai Institute of Science and Technology, Vallam, Tamilnadu, India,
Published: May 2020

In India Coconut is the major plantation crop in the states of Tamilnadu, kerala, Karnataka, Kongan region of Maharastra and Andaman and Nicobar Islands for entire seasons. Copra is the major product from the coconut cultivation earning higher income of small and medium livelihoods. The approval of copra quality is mainly based on how well the copra got dried. Open drying or other conventional methods is the major process of making copra. In adverse weather condition, rainy season the drying process will be very challenging. Many dryers are made and used currently was affordable to medium and large scale copra producers. Those dryers also having limitations in size, high initial cost and nature dependency. There is very few attempt made for Small and individual household copra producers. This paper mainly focuses on how to dry-up the copra in all climate conditions. An electric handy dryer is designed to dry up the coconut copra and other grains. It mainly helps the small scale farmers as a handy dryer unit to dry-up the copra, those who are using coconut as a way of income. Based on the experiments conducted the electric dryer removed high moisture content than forced convection and direct sun dryers.
Aerodynamic Performance of Road Vehicles
Authors:   Professor, Amity School of Engineering & Technology, Amity University, Lucknow Campus, Uttar Pradesh, India., Murtaza* Dr. M.A.
Published: May 2020

Aerodynamic drag has been experimentally estimated for scale models of a passenger car and a commercial truck in a wind tunnel. Polished surface has resulted up to 15 % reduction in drag force and add-on has resulted in 57% increase in drag force of a car model whereas 2.6 % reduction in drag force has resulted by using deflector in a commercial truck model. Anova analysis shows variation in mean of group data.
An Empirical Prediction Methodology for the Emotional Behaviors with the Impact of Musical Features
Authors:   student, Amrita VishwaVidyapeetham University., Integrated* Malini M, , Menon Kripa ,   student, Amrita VishwaVidyapeetham University., Krishnan M. Soumya ,   Faculty Associate, Dept. of CS &IT,AmritaVishwaVidyapeetham, Amrita School of Arts and Sciences.
Published: May 2020

Music is the combination of melody, linguistic information and singer’s mental realm. As popularity of music increases, the choice of songs also varies according to their mental conditions. The mental conditions reach the supreme bliss to melancholy strain based on the musical notes. Majority mostly prefer songs, which satisfy their current state of mind. Pragmatic analysis in music by computer is a difficult task, as emotion is very complex and it camouflages the real situation. Hence, In this paper , trying to classify the songs based on the features of music which helps to classify the emotion more easily. Music feature extraction is done using Music Information Retrieval (MIR) toolbox. The dataset consists of 100 of Hindi songs of 30 seconds clip and later classify the emotion based on Naïve Bayes classification method using Weka API.
Intrusion Detection System in Wireless Sensor Networks by using Lesser Algorithm
Authors:   Department of ECE, Sunrise University, Alwar, Rajasthan, India., Annareddy* FMaheswarareddy , Saini Prof. (Dr.) K.K. ,   Department of ECE, Sunrise University, Alwar, Rajasthan, India.
Published: May 2020

Wireless sensor networks (WSNs) are vulnerable to various types of security threats that may degrade the performance of the entire network. This problem could not provide protection for the attacks on key management and authentication protocols during fatal issues such as service denial attacks, routing attacks, sybil attacks and few other. Over the last few years, researchers have developed different distributed and centralized intrusion detection systems for wireless sensor networks but not reached to a higher performance level. A solution to this problem is the proposed Intrusion Detection System (IDS) with an implementation of more efficient algorithms that can perform routing decisions at every node. There are a couple of routing algorithms in the wireless sensor network which use topology data and takes routing decisions instantly. Extensions to the original position-based algorithm were subsequently presented to perform even more efficiently in conditions where they do not operate. Host growth brings about a route transition, which causes some network to settle on new routes. For ad hoc networks certain routing protocols have already been proposed. The fundamental idea is to allow cellular beneficiaries with bad channel conditions to use the ad hoc network to connect to those cellular collectors who experience great cellular channel conditions. The proposed system can also analyze the network by collecting enough data and detects irregular sensor node(s) behaviour. Finally, the current article explains the implementation of the lower algorithm in sensor networks for evaluating the network intrusion detection system.
Solar System Array by Fuzzy Logic based on MPPT Algorithm
Authors:   Dept of electrical engg., Azad Institute of engineering & Technology, Lucknow, India., Kumar* Mukesh , Asif Ali Mohd ,   Dept of electrical engg., Azad Institute of engineering & Technology, Lucknow, India., Alam Sanawer ,   Dept of electrical engg., Azad Institute of engineering & Technology, Lucknow, India.
Published: May 2020

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm.
Musenet : Music Generation using Abstractive and Generative Methods
Authors:   Department of Computer Science, SRM Institute of Science and Technology, Chennai, India., Pal* Abhilash , Saha Sourav ,   Department of Computer Science, SRM Institute of Science and Technology, Chennai, India., Anita  ,   Department of Computer Science, SRM Institute of Science and Technology, Chennai, India.
Published: May 2020

Humans have been entertained by music for millennia. For ages it has been treated as an art form which requires a lot of imagination, creativity and accumulation of feelings and emotions. Recent trends in the field of Artificial Intelligence have been getting traction and Researchers have been developing and generating rudimentary forms of music through the use of AI. Our goal is to generate novel music, which will be non-repetitive and enjoyable. We aim to utilize a couple of Machine Learning models for the same. Given a seed bar of music, our first Discriminatory network consisting of Support Vector Machines and Neural Nets will choose a note/chord to direct the next bar. Based on this chord or note another network, a Generative Net consisting of Generative Pretrained Transformers(GPT2) and LSTMs will generate the entire bar of music. Our two fold method is novel and our aim is to make the generation method as similar to music composition in reality as possible. This in turn results in better concordant music. Machine generated music will be copyright free and can be generated conditioned on a few parameters for a given use.The paper presents several use cases and while the utilization will be for a niche audience, if a easy to use application can be built, almost anyone will be able to use deep learning to generate concordant music based on their needs.
Securing Criminal Records using R-Pi, QR code and Steganography
Authors:   Department of Electronics Engineering, Shah & Anchor Kutchhi Engineering College, Mumbai., Durafe* Asha
Published: May 2020

In today`s digital scenario it has become very essential to maintain secrecy of criminal records otherwise forgery could happen. Using steganography it is possible to provide security for the information which is communicated over the internet from one crime branch to the other. Steganography one of the emerging security fields works to mask the very existence of the message. A wide range of carrier file formats can be utilized, but digital steganography is the extremely beneficial data hiding technique to secure criminal image as well as the crime scene images. Various applications have various prerequisites of the steganography method utilized. In this paper, we proposed CRSS (Criminal Record Security System) an image steganography method with LSB and RSA technique for enhanced security and along with that Raspberry pi and GSM module is used. Thus, for a more secure approach, the proposed method hides the criminal`s confidential records such as criminal`s image, crime scene digital images etc. using LSB steganography and also encrypts the confidential data making use of a private key using RSA algorithm and then sends it to the desired end. The receiver then decrypts the confidential data to get the original criminal information. CRSS is also proposed to send a QR code to the receiver which hides sensitive data and may include criminal`s previous crime history and other written proofs which are scanned at the receiving end reveals the entire criminal record. The entire system is implemented on Raspberry Pi 3 processor and thus a secure transmission of data without traditional desktop dependency in a more economical way could be established.
An Experimental Test and Optimization of Friction Stir Welding Process for AA6082 By using GRA
Authors:   Assistant Professor, Department of Mechanical Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Periyaseeragapadi, Salem, A constituent college of Vinayaka Mission’s Research Foundation, Deemed to be University, Salem, Tamil Nadu, India., kumar* M Senthil , Venkatesan S ,   Professor, Department of Mechanical Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Periyaseeragapadi, Salem, A constituent college of Vinayaka Mission’s Research Foundation, Deemed to be University, Salem, Tamil Nadu, India., Samuel C Bright ,   Department of Mechanical Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Periyaseeragapadi, Salem, A constituent college of Vinayaka Mission’s Research Foundation, Deemed to be University, Salem, Tamil Nadu, India.
Published: May 2020

Friction stir welding (FSW) is a type of joining process, it uses solid state welding method, also it is widely used in same type and different types of welding like Al, Mg, Cu, Ti, and their alloys. In this study, friction stir welding of two aluminum alloys AA6082 is done with many sets of tool rotation speed, feed and axial force. In this experimental work FSW process was carried out for AA 6082 and optimization of that FSW process parameters were find out for maximum tensile strength values. Taguchi’s L4 orthogonal array was utilized for three parameters – tool rotational speed (TRS), traverse speed (TS), and axial force (AXF) with two levels. Several optimization was carried out with Taguchi method of grey relational tests. During the investigation obtained highest tensile strength value fourth sample 60.887 N/mm2 and lowest hardness strength value second sample 31HRB and bead appearance found very best surface occurred fourth test plates at the same time angle distortion observed very fine in the fourth test plate. The result was calculated for both ultimate tensile strength and hardness value. The expected grey relational grade was shifted from 0.704 to 0.792, it was the highest value received throughout this experimental results. It was mentioned that the multi-responses of FSW process was improved with this method.
Vehicle Number Recognition From Vehicle Images using CNN
Authors:   Assistant Professor, Dept. of Computer Science and Engineering), SRM Institute Of Science and Technology, Chennai, India., Krithika* Dr.S Usha , Varma M.Ramana Sai Prasada ,   Dept. of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India., Sukesh Y. ,   Dept. of Computer Science and Engineering, SRM Institute Of Science and Technology, Chennai, India.
Published: May 2020

Automatic vehicle number recognition is used to recognize the vehicle number plate from images of vehicles and also the number from the number plate. It plays an important role in some applications such as automated parking, over speed tracking of vehicles, road traffic monitoring and issuing fine for traffic rule violators. Several challenges like number obstruction, faded numbers, blurring, time consumption, etc. are involved in number recognition from real-time images. We survey the techniques that have been applied for this problem. We also provide a classification of the techniques which will be useful for researchers to identify research gaps in this area. Finally, we provide future scope for research in this area.
An Exploration into Energy Efficient Algorithms Based on the Techniques of Particle Swarm Optimization in MANET
Authors:   Assistant Professor, Dept. of Computer Science, Government First Grade College, Tumkur, Karnataka, India., S* Haridas. , Prasath.A Rama ,   Assistant Professor (Selection Grade), Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai, India
Published: May 2020

This paper throws understanding into nature inspired meta-heuristic algorithm; Particle Swarm Optimization based technique in designing various energy efficient MANET routing algorithms. Mobile ad hoc network (MANET) is a framework-less system of self-directed mobile nodes. Owing to the dynamic topological property of MANET, it’s very rigid task to calculate the performance of routing protocol under unpredictable network conditions and the performance of the network will degrade. The performance of Mobile Ad-hoc network is measured by metrics such as packet delivery ratio, battery power and total delay in delivery. Regular variation in topology affect route selection and lifespan of network, hence it is essential to optimize these metrics parameters. There are various protocols developed for routing. These routing paths are established to send data packets through the shortest path without a satisfying multi-objective approach. The purpose of the research is to discuss different approaches of swarm based intelligence routing algorithms used in MANET under dynamic conditions by comparing the measured metrics and to design a novel effective PSO based routing procedure for MANET.
Enhancing the Classification of Pap Smear Images using ENN – TLBO classification Method
Authors:   Assistance Professor, Charan’s Degree college. Bengaluru, Karnataka.,India., Geetha*  , Suganya S. ,   Associate Professor, RVS College of Arts and Science, Sulur, Tamil Nadu.,India.
Published: May 2020

The most common cancer among the women younger than 35 in developing countries is cervical cancer. It is a human papilloma virus disease. It should be identified earlier by Pap smear test and treated earlier to avoid the consequences. Pap test a colonoscopy is widely used to check the vagina and the cervix. The Pap smear test is the most effective medical test, but it causes difficulty under the microscope at the point of analysis. Automatic cancer detection is designed to unravel the downside. This identification process involves some image processing methods, such as segmentation, and an improved SVM classification method. In this paper, an efficient Elman Neural Network (ENN) collaborating with Teaching Learning Based Optimization (TLBO) algorithm is proposed to classify cancer using Pap Smear Test images. At first, an input image of Pap smear is converted into grey level from the RGB. The grey level image is preprocessed to eliminate unwanted noise produced and smoothened with Kuan Filter (KF). Active Contour Method (ACM) has been used to segment the identified cells from the Pap smear image. Features such as GLCM, haralick, solidity, shape, and other mathematical features are extracted for improving the accuracy. Classification has done using ENN-TLBO. TLBO is utilized for getting optimal weights during the training phase. Performance evaluation has done through the experimental outcomes where, ENN-TLBO yields good accuracy of 86.6%, than the prevailing algorithms such as Support Vector Machine, Radial Basis Function Neural Network classifiers.
Machine Learning in Agriculture Application: Algorithms and Techniques
Authors:   Dept. of CSE M S Ramaiah Institute of Technology Bangalore,India., Meeradevi*  , N Sindhu ,   Dept. of CSE M S Ramaiah Institute of Technology Bangalore,India., Mundada Monica R ,   Dept. of CSE M S Ramaiah Institute of Technology Bangalore,India.
Published: May 2020

Machine learning techniques with high performance computing technologies can create various new opportunities in the agriculture domain. This paper does comprehensivereview of various papers which are concentrating on machine learning (ML) and deep learning application in agriculture. This paper is categorized into three sections a) Yield prediction using machine learning technique b) Price prediction c) Leaf disease detection using neural networks. In this paper we study the comparison of neural network models with existing models. The findings of this survey paper indicate Deep learning models give high accuracy and outperform traditional image processing technique and ML techniques outperforms various traditional techniques in prediction.
Video to Text conversion and Abstractive Summarization for Effective Understanding and Documentation
Authors:   CSE Department, GNITS, Hyderabad, India., Swapna* T. , Devi Y.Sravani ,   CSE Department, GNITS, Hyderabad, India., Sindhura K. ,   CSE, GNITS, Hyderabad, India
Published: May 2020

Video is one in every of the sturdy sources of data and the consumption of on-line and offline videos has reached a new level within the previous few years. An elementary challenge of extracting data from videos is, a viewer should undergo the whole video to grasp the context, as against a picture wherever the viewer will extract data from one frame. Typically, protracted videos also are quite troublesome to follow because of reasons like totally different pronunciation, pace then on. Abstractive Text summarization extracts the utmost important information from a source which is a text and provides the adequate outline of an equivalent. The analysis work conferred during this paper describes a straightforward and effective methodology for video Summarization. It principally targets academic and technical videos. Speech is extracted from video. The speech is regenerate to the corresponding text using abstractive summarization technique and produces summarized text. For quicker conversion of video to text GPU can be used. This has numerous applications like lecture notes creation, summarizing catalogues for protracted documents then on.
Trash Boat for Floating Waste Removal
Authors:   ECE, Sri Krishna College of Engineering and Technology, Coimbatore,India., Saranya* G. , Dharsheeni R. ,   ECE, Sri Krishna College of Engineering and Technology, Coimbatore,India., Chitrarasu V. ,   ECE, Sri Krishna College of Engineering and Technology, Coimbatore,India., Gokuldharan S. ,   ECE, Sri Krishna College of Engineering and Technology, Coimbatore,India., Gokulraj P. ,   ECE, Sri Krishna College of Engineering and Technology, Coimbatore,India.
Published: May 2020

Pollution plays a major part in global warming and water pollution stands alarming for all living organisms in the world. Water pollution is killing tens of millions of Indians and nearly 19.3% of Indian population does no longer have access to clean water. The toxicity of Indian water our bodies are increasing except India’s boom and urbanization. It is located that out of general water available, round 70% of surface water in India is not worthy for consumption. India has a mortality fee of 400000 lives according to year because of lack of sanitation and hygiene. Researchers have found that the closing purpose of water pollution is the plastics. In order to overcome these issues, a smart trash boat is designed in this paper which can accumulate all the floating and semi floating wastes specifically plastics from the urban drain from any water bodies. This system is enabled with Artificial Intelligence and image processing which is capable of classifying, managing, accumulating and indicating the status of trashes along with its statistics.
Cooperation with Turkey in Development of Tourism Sector in Uzbekistan
Authors:   Independent researcher Research are concerns social innovations and international relations, Khojanazarovich* Imomov Bobur , Ruzimurotovna Ochilova Nigora ,   candidate of philosophy sciences, ass.professor. main scientific research field is socio-philosophy. She has more than 50 articles on socio-philosophy and 9 international publications., Ergashevich Ibragimov Solijon ,   senior teacher. Research field deals political sciences. He has published more than 30 articles and 5 international articles., Xalilovna Turayeva Sanobar ,   senior teacher. Research field deals social sciences. she has published more than 20 articles and 6 international articles.
Published: May 2020

This article outlines the issues of cooperation between the Republic of Uzbekistan and the Republic of Turkey in the field of tourism, the legal framework for the development of tourism in Uzbekistan and projects implemented by entrepreneurs of the two countries, practical issues, and key issues in the field of industry development.
Recognization and Systematization of MR Imagesusing K Means Clustering and DNN
Authors:   M. E, Head Of Department, Computer Science Engineering, Prince Dr K. Vasudevan College of Engineering And Technology, Ponmar, Chennai, India., Deepa* R. , Lifty S. Ashlin ,   U.G Graduate, Computer Science Engineering, Prince Dr K. Vasudevan College of Engineering and Technology, Ponmar, Chennai, India., Deepigka M. S. ,   U. G Graduate, Computer Science Engineering, Prince Dr K. Vasudevan College of Engineering and Technology, Ponmar, Chennai, India.
Published: May 2020

Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.
Application of GIS and Remote Sensing Technology to Detect Landscape Change in Hollongapar Gibbon Wildlife Sanctuary, India
Authors:   Research scholar, Department of Geography, Gauhati University, Guwahati, India., Ahmed* Rekib
Published: May 2020

The present study stated on an evaluation into the use of remote sensing technology and geographic information systems (GIS) integration to detect land use/cover trajectories in Hollongapar gibbon wildlife sanctuary in Assam, India. Remote sensing technology was used to utilize multi-temporal satellite imagery including Landsat TM (Themetic Mapper) and Landsat OLI (Operational Land Imager) data to perform LU/C change detection from the year 1986 to 2018. The results revealed significant and unequal land conversion in the region of study. The paddy fields and tea gardens in and around the 4 km buffer of the wildlife sanctuary had increased sharply during the period 1986-2018. Remote sensing and GIS integration has been found to be effective in tracking and analyzing trends of LU/C trajectories and assessing the effects of land conversion on biodiversity of the study region.
Influence of Mwcnt’s/Zinc Oxide Nano Particles/Epoxy Resin Composite Coating on Mild Steel to Enhance Anticorrosion and Mechanical Properties
Authors:   Assistant Professor, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, Dist. Raigad - 410207, Maharashtra, India., Gujjar* Sandeep.V. , Nadar Dr. Nandini R ,   Assistant Professor, Department of Mechanical Engineering, B.M.S.Institute of Technology and management, Bangalore-64, Karnataka, India., Chaudhary Kanaram ,   Civil and infrastructure engineering from Rustomjee Academy for Global Careers, Dahanu road (E) - 401602, Maharashtra, India, Hunashyal Dr. Anand. M. ,   Associate Professor, Department of Civil Engineering, Visvesvaraya Technological University, KLE Institute of Technology, Hubballi-580021, Karnataka, India.
Published: May 2020

The motive of this research is to develop a new hybrid Nano composite on the surface of mild steel to eradicate the rate of corrosion that takes place on the surface of the mild steel and to enhance mechanical properties. For this, a hybrid Nano composite of Multi-Walled Carbon Nano Tube’s (MWCNT’s), Zinc Oxide (ZnO) Nano particles and Epoxy resin has been used to overcome the major disadvantage of mild steel which is corrosion. The mechanical property of mild steel is also increased. Ultra-sonication method is adopted for better dispersion of ZnO Nano particles and MWCNT’s. In this study, Ethanol is used for better dispersion. After applying the coating on the surface of newly developed Nano composite by using pneumatic gun spray method is used. FESEM was conducted to study the surface morphology of corroded surface of mild steel. The rate of anticorrosion and mechanical properties get improved by the application MWCNT’s/ZO Nano particles/Epoxy resin.
Predicting Churn Customer in Telecom using Peergrading Regression Learning Technique
Authors:   Professor, Ramakrishna College of Arts & Science Coimbatore, Latha* Dr. M. Hema , Mahalakshmi S. ,   Research Scholar, Bharathiar University,Coimbatore.
Published: May 2020

Customer churn is an important issue which is faced by telecom industries daily. It is an essential concern for enterprises. Most of the telecom companies suffer a lot for voluntary churn. Here the churn rate is a significant impact for the industries on the lifetime value, and it affects the length of the service and also the revenue of the company. Because of the direct effect of the company revenue, especially in the telecom field, the companies are requesting to implement the development of predicting the potential customer to churn. Here the churn is an essential factor. Hence, analyzing the churn factor of increasing customer churn is as necessary as it is needed to reduce the churn. Recently telecom service facing the churn problem, for this analysis this research article focused on prediction of customer churn. In this research work, the main contribution is to develop a prediction model of churn, that supports the telecom operators to predict the customer, who is going to be churn. Here the model used is the Machine learning technique with the big data analysis. Our methodology used here is Peer Grading Regression (PGR). To provide a prediction of churn customer in telecom industries. To validate the performance of this proposed model, the Area under Curve (AUC) is used, and it is one of the standardized approaches. This model will be tested under some benchmark dataset; working on these vast datasets creates a transformation of raw data by telecom companies. The dataset values are provided for testing and training in the ensemble classifier. For experimentation, four algorithms are considered here: Peer Grading Regression (PGR), Random Forest, Decision Tree, and k-NN classifier. Moreover, the best outcomes are attained with the use of the boosting algorithm. Here this algorithm is applied for the classification of the churn prediction model.