bluuebunny/crossref_metadata_2025 huggingface dataset:
Attendance Maintaining and Monitoring using Face Recognition
Authors:   Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India., Kalpana* C. , Hemavathi S. ,   Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India., Geerthana K. ,   Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India., Dhakshayini T. ,   Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India.
Published: May 2020

Technology has been playing a vital role in this world, where the work and the work place become digitalized. The paper reviews on monitoring the attendance using image processing, which involves face detection, labeling the detected face, training a classifier based on labeled dataset, and face recognition. Former methods on monitoring the attendance includes signing the attendance registry, fingerprint detection and barcode scanning where delinquency may occur. To prevail over and to take the technology to subsequent level image processing has been incorporated. Proposed system employs, capturing of the face in various dimensions, labeling of the captured images that is stored in the database for training and testing phase. Using the gathered data the machine is trained to recognize the face to provide access to the employees or students in the organization. The final phase is to take the attendance and maintain the record on attending hours using face recognition technique in which the input image of the employees or students is given.
DNA Classification using Machine Learning for Detecting Genetic Disorders
Authors:   Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India., Mishra Amisha , Duggal Shruti ,   Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India., Banerjee Snehanshu ,   Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India., Sarooraj Mr. R B ,   Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
Published: May 2020

Deoxyribonucleic acid is a double- helical molecule composed of two chains that contains genetic instructions. Genetic diseases are caused by changes in pre-existing genes. A genetic abnormality results from the alteration in chromosomes. DNA classification helps to identify genetic disorders in organisms. DNA pattern recognition is a major issue in bioinformatics. DNA is classified into several categories on the basis of Structure, Location, Number of base pairs etc. Traditionally the DNA Molecule is studied by extracting it from the blood sample and is then manually analysed to find out the abnormalities. To increase the accuracy, a machine learning based DNA classification is done which helps in studying the extracted DNA image using various techniques. This consumes minimal amount of time and is more efficient. The image is pre-processed using median filter and canny edge detection. DNA sequences can be recognized correctly and effectively without any uncertainties with the help of Neural Network.The network successfully classifies an image given as input when it is trained with patterns. Thus, we can analyse if a person has a genetic disorder.
Trust Based Routing in Wireless Sensor Network
Authors:   Associate Professor, Department ECE, NMIT Blore., Patil* Dr. Madhu , M.A Dr. goutham ,   Professor and Head of Department, Department of ECE, AIT, Chickmagalur.
Published: May 2020

The nodes are often placed in a hostile or dangerous environment so WSN will be vulnerable to security attacks. Attacks can be active or passive. Passive attack involves unauthorized attackers who listen to the communication channel and monitor its activities. In active attack, the unauthorized attacker’s listens, monitors and alters the data passing through the communication channel. To address security concerns, cryptography approach has been adopted but this alone cannot cope with some of the routing attacks. As sensor devices are resource constrained, existing cryptography approach may not be applicable, due to the computational complexities that it involves [1] [3]. Hence, author present energy efficient trust based routing strategy, which is a viable alternative for WSN.
Sentiment Analysis of Events on Social Web
Authors:   Deptt of Computer Science Engineering, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, India., Garg* Neha , Sharma Dr. Kamlesh ,   Deptt of Computer Science Engineering, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, India.
Published: May 2020

These days, Data volume has experienced enormous increase in volume, giving new challenges in technology and application. Data production has been expected at the rate of 2.5 Exabyte (1Ex-abyte=1.000.000Terabytes) of data per day. The main sources of data are: sensors collect climate information, traffic and flight information, social media sites (Twitter and Facebook are popular examples), digital pictures and videos (YouTube users upload 72 hours of new video content per minute), etc. Out of them social media becomes the prominent representative for the data source of big data. Social big data comes from the combination of social media and big data. Here, the data is mostly unstructured or semi-structured. The classical approaches, techniques, tools and frameworks for management of data have become insufficient for processing this huge volume of data and not capable for providing efficient solution to handle the increased production of data. The major challenge in data mining of big data is, its inadequate approaches to analyze massive amount of online data (or data streams). Specially, the field of sentiment analysis and predictive analysis has become so much promising area to place an organization at doom or at boom by provide accurate decision at accurate time. The current paper provides an insight of machine learning algorithm both supervised and unsupervised method; and the traditional knowledge extraction process. The application field of sentiment analysis, the issues those are faced during data collection and cleaning. This study flourishes a complete picture of recommendation system based on the sentiment analysis of events. The key motivation of the paper is to incorporate the event sentiment analysis and give the feedback and recommendation and illustrate the ongoing researches in the field of sentiment analysis and its application.
Identifying Intrusion Behaviour using Enhanced Hidden Markov Model
Authors:   Teaching Asst, Dep of CSE, SRM University, AP, India., Purnima* T , Delhipolice Chandu ,   Assistant Professor, Priyadarshini Institute of Technology & Science. Tenali, A.P., Sarada K. ,   Research Scholar, Dep of IT, VFSTR, Vadlamudi, AP
Published: May 2020

Data Mining is a method for detecting network intrusion detection in networks. It brings ideas from variety of areas including statistics, machine learning and database processes. Decreasing price of digital networking is now economically viable for network intrusion detection. This analysis chiefly examines the system intrusion detection with machine learning and DM methods. To improve the accuracy and efficiency of SHMM, we are collecting multiple observation in SHMM that will be called as Multiple Hidden Markov Model (MHMM). It is used to improve better Detection accuracy compare with SHMM. In the standard Hidden Markov Model, we have observed three fundamental problems are Evaluation and decoding another one is learning problem. The Evaluation problem can be used for word recognition. And the Decoding problem is related to constant attention and also the segmentation. In this Proposed Research, the primary purpose is to model the sequence of observation in Network log and credit card log transactions process using Enhanced Hidden Markov Model (EHMM). And show how it can be used for intrusion detection in Network. In this procedure, an EHMM is primarily trained with the conventional manners of a intruders. If the trained EHMM does not recognize an incoming Intruder transaction with adequately high probability, it is thought to be fraudulent.
Using Latent Class Analysis to Identify Political Behavior of Moroccan Citizens on Social Media
Authors:   Laboratory of Engineering Sciences and Modeling, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco., Zakia* Acharoui , Alaoui Altaf ,   Laboratory of Engineering Sciences and Modeling, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco., Azhari Mourad ,   Laboratory of Engineering Sciences and Modeling, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco., Abarda Abdallah ,   Laboratory of Mathematical Modelling and Economic Calculations, FSJES, Université Hassan 1er, Settat, Morocco., Ettaki Badia ,   Laboratory of Research in Computer Science, Data Sciences and Knowledge Engineering, Department of Data, Content and knowledge Engineering School of Information Sciences, Rabat, Morocco., Zerouaoui Jamal ,   Laboratory of Engineering Sciences and Modeling, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco.
Published: May 2020

The high use of social media has led to a new form of political involvement and participation. In this paper, we use Latent Class Analysis to identify participants’ behavior regarding political participation and engagement based on the nature of their interaction on social media. The LCA findings reveal three statistically distinct and behavioral classes regarding political interaction on social media. The profiles were ranged from ‘Activist’ that show more engagement in political activity, such as following candidates and political parties, posting and participating in discussions related to economic, social or political issues or, encouraging others to debate their point of view, to ‘Agitator’ and ‘Outsider’ profiles that show a low probability of interacting on social media and engaging in political actions. The LCA technique has provided meaningful and distinct information on the participants’ political profile than clustering classical techniques.
A YOLO based Technique for Early Forest Fire Detection
Authors:   Student, Bachelors of Engineering, computer science and engineering integrated with MBA, Thapar Institute of Engineering & Technology, Goyal* Mr. Sidhant , Shagill Mr. MD ,   Student, Bachelors of Engineering, computer science and engineering integrated with MBA, Thapar Institute of Engineering & Technology, Dhillon Ms. Arwinder ,   Ph.D, Computionational Bioinformatics in Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala. Punjab, India., Vohra Dr. Harpreet ,   Assistant professor, Electronics and Communication Engineering Department, Singh Dr. Ashima ,   Assistant Professor, Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala
Published: May 2020

Forest fires, wildfires and bushfires are a global environmental problem that causes serious damage each year. The most significant factors in the fight against forest fires involve earliest possible detection of the fire, flame or smoke event, proper classification of the fire and rapid response from the fire departments. In this paper, we developed an automatic early warning system that incorporates multiple sensors and state of the art deep learning algorithm which has a minimum number of false positives and give a good accuracy in real time data and in the lowest cost possible to our drone to monitor forest fire as early as possible and report it to the concerned authority. The drones will be equipped with sensors, Raspberry pi 3, neural stick, APM 2.5, GPS, Wifi. The neural stick will be used for real time image processing using our state-of-the-art deep learning model. And as soon as forest fire is detected the UAV will send an alert message to the concerned authority on the mobile App along with location coordinates of the fire, image and the amount of area in which forest is spread using a mesh messaging. So that immediate action will be taken to stop it from spreading and causing loss of millions of lives and money. Using both deep learning and infrared cameras to monitor the forest and surrounding area, we will take advantage of recent advances in multi-sensor surveillance technologies. This innovative technique helps the forest department to detect fire in first 12 hours of its initialization , which is the most effective time to control the fire.
Security of Drone Hacking with Raspberry-Pi using Internet-of-Things
Authors:   M. Tech in CSE from Prist University and MCA from Bharathidasan University, India, Marimuthu* Pratheepa , Sekar Saran Kumar ,   M.E. in Aeronautical Engineering, Lecturer-Department of Aeronautics, St. Theresa International College, Nakhhonnayok - Thailand,
Published: May 2020

Internet-of-Things (IoT) implementation in technological application is developing at higher speed because of more demand from the customers and firms which reply with the advantages proposed through the brilliant and elegant hardware unit. Using Drone concept, it is finding more applications in various areas which rises the threat of data hacking and also poses safety risk to common people. We must appreciate due to online data collection from the application of commercial drones which is widespread across varieties of drone use. Implementation of IoT enabled drones in large quantity causes more number of drones which is exposed to the possibility of being attacked or harmed by the fraudster. This research work examine the various issues concerned to drone security, safety, threat, attacks and illustrate a group of wireless fidelity risk possibilities. My research work has observed different types of threat and attacks on commercial drones. The propagation channel risk analyzed are, Access Denial, Authorization techniques, unauthorized third party presence, illegal source access gain and frame misuse of device characteristic features. Our work prohibits illegal connection entry controlled based on Raspberry-pi & wireless fidelity between access point and drone happens using tell-net with the help on IP address and sign up process. The investigation work explains the execution of methodology which includes attack steps and data security enabling using RSA Algorithm. The main work focuses on the data collected on parrot security OS and to secure the communication in Wi-Fi mode using drone and another devices were analyzed.
MFO-ANN based FOPID Controller for Torque and Speed Control of SRM Drive System
Authors:   Research scholar, Electrical Engineering, JNTUA University, Ananthapuramu, Andhra Pradesh, India., Kumar* K. Vinod , Reddy Dr.P. Ram Kishore Kumar ,   Professor and Head of the Department of Electrical and Electronics Engineering at MGIT, Hyderabad, India., Sujatha Dr. P. ,   presently working as a Professor in Department of Electrical Engineering, J.N.T.U. A. College of Engineering, Ananthapuramu, Andhra Pradesh, India
Published: May 2020

This paper proposed an optimal control algorithm based Fractional Order PID (FOPID) controller for the torque ripple minimization of Switched Reluctance Motor (SRM) drive system. That convalesce both control loop task, the Moth Flame Optimization (MFO)- Artificial Neural Network (ANN) algorithm is insinuated. The routine of intelligent technique is principally as the objective function of system error minimization. At this time the quality inspired algorithm of MFO is firstly reconnoitered to optimize the speed and torque error from the SRM system. Instead the output of MFO algorithm is optimized and to finding the premium value, the ANN method is recuperated. Nonetheless, the output of MFO-ANN is imperiled to the input of FOPID controller. For tweaking the exact FOPID gains, the multi objective functions are grew. Created on the operation of propositioned algorithm by normalize the system speed and minimalize the torque ripples of SRM system. The qualities of the suggested procedure are force falconer and in addition to augmented level of the reliability and flexibility in answering the system error. Also the enactment of proposed MFO-ANN process is executed in the MATLAB/Simulink running platform.
Influence of Artificial Aging on the Dry Sliding Wear Behavior of ADC12 Alloy-B4C-Rice Husk Ash Particulates Reinforced Hybrid Composites
Authors:   Department of Mechanical Engineering, University Visveswaraya College of Engineering Bangalore-560001, Karnataka, Inida., Mohan* R Murali , Kempaiah U N ,   Department of Mechanical Engineering, University Visveswaraya College of Engineering Bangalore-560001, Karnataka, Inida., Nagaral Madeva ,   Aircraft Research and Design Centre, Hindustan Aeronautics Limited, Bangalore-560037, Karnataka, India.
Published: May 2020

The influence of artificial aging on the wear behavior of ADC12 alloy reinforced with Boron carbide (B4C) and Rice Husk Ash (RHA) composites have been investigated. Hybrid composites with 5 wt. % of B4C fortification constant and variable quantity of rice husk ash particles in steps of 9 and 12 wt. % in the ADC12 alloy prepared by melt stir process. ADC12 aluminium alloy, ADC12 alloy-5 wt. % B4C-9 wt. % RHA and ADC12 alloy-5 wt. % B4C-12 wt. % RHA Samples were solutionized at a temperature of 525˚C for 1 h. Further, these solution heat treated samples were artificially aged at the temperature of 175˚C for 10 h. Microstructural characterization was carried out by using SEM and EDS. A pin-on-disc wear testing machine was utilized to assess the wear loss of specimens, in which a solidified EN32 steel plate was utilized as the counter face. Wear tests were accompanied on ADC12 alloy, ADC12 alloy-5 wt. % B4C-9 wt. % RHA and ADC12 alloy-5 wt. % B4C-12 wt. % RHA hybrid composites at varying loads of 10 N, 20 N and 30 N with varying sliding distances of 250 rpm, 500 rpm and 750 rpm for constant sliding distance of 1000 m. The wear resistance of ADC12 alloy enriched with the accumulation of B4C and RHA particulates. Further, heat treated samples were exhibited the superior wear resistance as compared to the base alloy and un-heat treated samples.
Corrosion and Inhibitor on Material: Experimental and Computational Calculations
Authors:   Faculty of Engineering and Applied Sciences, Mechanical Engineering Department, PNG University of Technology, Lae, MP 411, Papua New Guinea., Mohamed* Aezeden , Alrawashdeh Ahmad ,   Department of Physics and Physical Oceanography, Memorial University of Newfoundland, St. John’s, NL, A1B 3X7, Canada, Rahoma Abdalhamid ,   Faculty of Engineering and Applied Sciences
Published: May 2020

In this paper, we provide a comprehensive study of corrosion inhibition of mild steel in 1 M HCl solution at 313, 323, and 333 K using 4,4-Dimethyl oxazolidine-2-thione (DMOT) and its protonated form (DMOTH+) as inhibitors. Our results show that the corrosion rate of mild steel in 1 M HCl increases as the temperature increases while it decreases as the DMOT concentration increases. In contrast to the corrosion rate, the DMOT inhibition efficiency decreases with temperature and increases with increasing DMOT concentration. Both experimental and quantum chemical computational results reveal that the adsorption of DMOT and DMOTH+ on the mild steel surface is a mixed-type process having both physisorption and chemisorption. Moreover, the inhibitor adsorption on the mild steel surface was found to obey the Langmuir adsorption isotherm and the value of Gibbs energy of adsorption at the three studied temperatures is associated with an adsorption mechanism involving both physisorption and chemisorption processes. Heavy corrosion, cavities, and pitting of surfaces were observed in the absence of DMOT inhibitor, while much less corrosion was consistently observed in the presence of DMOT inhibitor.
Classification of Eye Disorders based on Deep Convolutional Neural Network
Authors:   Department of Computer Science and Engineering, SRM Institute Of Science And Technology, Kattankulathur, India., Dondapati* Chandra Lekha , Ghosh Ashutosh ,   Department of Computer Science and Engineering, SRM Institute Of Science And Technology, Kattankulathur, India., Malleswari Dr. TYJ. Naga ,   Department of Computer Science and Engineering, SRM Institute Of Science And Technology, Kattankulathur, India.
Published: May 2020

Multiple Eye Diseases are currently diagnosed visually by ophthalmologists. In the beginning period, the huge scope screening of eye diseases is outlandish since there are less number of ophthalmologists and in addition these strategies expend additional time. This indicates that in order to correctly identify the disease, manual intervention and the proper infrastructure is important. Owing to the fact that many developing nations are not able to provide their masses with the basic healthcare facilities, the need for computer-aided systems that are robust and inexpensive increases manifold. Over the last few years, convolutional neural networks (CNN) are being increasingly employed for the task of automatic and semi-automatic image classification. Through this paper, we aim to develop a method using deep learning architecture to detect eye disorders in fundus images. In the initial step preprocessing is accomplished for the fundus image, trailed by feature extraction and order. Different evaluations of influenced pictures are tried by the proposed technique and the presentation has been looked at and examined. The models would be tested using standard evaluation metrics to evaluate the effectiveness of the models.
Development of Easyget Algorithm for Deep Learning Sculptor Deepcnet Model using Hadoop Architecture
Authors:   Ph. D. Research Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P.India., Patil* Ms.Jyoti , Pradeepini* Dr.G. ,   Professor, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P.India.
Published: May 2020

Manual segmentation in the brain tumors analyses for malignancy prognosis, via massive amount MRI images produced through medical routine, frustrating task and is a hard. There is a dependence on automated brain tumor graphic segmentation. The amount of precision necessary for scientific purposes is normally as yet not known, and so can't be conveniently quantified actually by means of professional physicians. That is a fascinating point, which includes just sparsely been resolved in the literature, but is nonetheless truly relevant up to now. Additionally, storage space automatization for medical images is essential need nowadays. To carry out very quickly analysis as well as, prognosis there's an imperative want of automated photo storage. Hence, this paper focused on development of new algorithm called “EasyGet” for automatic data storage and retrieval using Hadoop architecture.
Performance of Waste Fried Oil based Biodiesel in a Stationary Diesel Engine
Authors:   Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India, Chowdary* Dr R.P. , Janardhan Dr N. ,   Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
Published: May 2020

The research activities related to alternative fuels has gained prominence, due to exhaustion of the present fossile fuels and rise in pollution levels. Any substitute to diesel will help mankind for safeguarding the environment due to redced usage of conventional fuels, as diesel consumption is high in goods transshipment sector and cultivation fields.Suitable replacement for diesel are Vegetable oils, because their cetane value is nearer to pure diesel. The combustion related issues with vegetable oils, either edible or nonedible can be resolved to a major extent by esterifying them.For this study the biodiesel extracted from used cooking oil, was used as fuel, in a 3.68 kW, 1500 RPM stationary diesel engine at distinct injection timings.The injection timings were changed by keeping copper shims between body of fuel pump and frame of diesel engine. The value of optimum injection timing(where maximum efficiency is obtained) was observed to be 310 forbiodiesel and diesel fuels. The performance parameters, combustion characteristics and pollution levels were recorded, at recommended and optimum timings of injection of 270 and 310 before the top dead centre(bTDC).Studies were conducted with pure diesel and biodiesel, made using waste fried vegetable oil (WFVOBD). The biodiesel showed equivalent performance at both manufacturer recommended and experimentally obtained optimum injection timings, but marginally increased levels of NOx
Automation and Process Control of High Performance Concrete Production by using Visual Interactive Analysis Method
Authors:   PhD, Lecturer at Le Quy Don Technical University, 236 Hoang Quoc Viet Str., Hanoi, Vietnam., Nguyen* Minh-Hong , Dang Hoang-Minh ,   PhD, Lecturer at Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao, W. 4, D. Go Vap, Ho Chi Minh City, Vietnam.
Published: May 2020

High performance concrete (HPC) has been used widely for many applications such as high-rise building, highway bridges, tunnels, ect. Although up to the present time there are many recommendations and guidelines issued from research institution on the method how to achieve HPC mix proportion, around the world engineers often spent a lot of time to do preliminary experimental tests in order to select the optimal mix especially when dealing with tailored-made HPC. This paper presents an innovative visual interactive analysis method (VIAM) with the application of parameter space investigation, which helps engineer efficiently in automation and process control of proportioning HPC mix. Component content of tailored-made HPC is considered as a solution of a mathematical model with six objective functions controlling by ten constraints and seven parameters. Indeed, VIAM has played an important role in successfully determining 18 Pareto optimal solutions of this complex model for three particular scenarios set by engineers.
New Segregation Method of Sic During Carbo-Thermal Silica Reduction
Authors:   Graduate School of Engineering and School of Engineering, Nagoya University, Nagoya, Japan, Boucetta* Abderahmane
Published: May 2020

The focus on SOG-Si for solar cell production have been increased in the recent years. A low cost Si requires a production with less electric energy used, less impurities and fast process. Reducing C presence in Si ingots generally is necessary. In the present work, we attempted to develop a new method to segregate SiC from the bulk Si by means a new shape of ingots that we designed. We have used a conventional induction furnace for this process. A comparison study between 2 shapes of graphite ingots was performed. We have succeeded in segregation of SiC from Si during the carbo-thermal reduction process of silica. the results revealed that The first ingot contained 34.3% of SiC while the second ingot had 100% of Si from about 13mm distance from the ingot bottom.
Smart Street Lighting System with Adafruit Cloud
Authors:   School of Electronics Engineering (SENSE), B. Tech, ECE with specialization in Internet of Things and Sensors, Vellore Institute of Technology, Vellore, India., Karthik* Velivala Pavan , Pusuluri Anirudh Lalithkalyan ,   School of Electronics Engineering (SENSE), B. Tech, ECE with specialization in Internet of Things and Sensors, Vellore Institute of Technology, Vellore, India., CV Ravi Kumar ,   School of Electronics Engineering (SENSE), Senior Assistant Professor, Vellore Institute of Technology, Vellore, India.
Published: May 2020

Street Lights consume a lot of power overnight, even though they do not appear to. This paper describes a way to reduce that power consumption while also storing the data in the cloud. In a fast trending world, storing data on the cloud is good for better maintenance and monitoring. This paper also gives a method to control the lights remotely, if required. The lights will be ON in the evening from dusk, and will remain ON in high density traffic areas, till late night. Then, the time when traffic reduces, lights will start working autonomously. This traffic intensity and timings will vary from place to place. These street lights communicate via the fields in the cloud, and will ON the lights before the vehicle even reaches the next area, for a safe margin and to prevent accidents. The main aim of this proposal is to not only reduce power consumption of unused street lights at night, but to also implement it with the help of a cloud to get data for future implementations, which will help make a better cloud-based system in the future.
A Self-Assurance Method Based on Trust Estimation for Secure Routing in MANET
Authors:   Assistant Professor in Department Computer Science at University College for women(A), Osmania University., Reddy* T.Shekar , Devi Y.Rama ,   Ph.D in Computer Science and Engineering from Central University of Hyderabad, India.
Published: May 2020

The improved edition of conventional wireless networks provides a mobile temporary network (MANET), which is extremely appropriate for urgent situations. However, in a similar instance, its infra-low and resource limit creates many challenges in its performance. The growing security risks are probable to take place due to dynamic behavior and the absolute communication cycle which are based on unnoticed nodes, which dropped packets as they desire. Reliable and trusted nodes can reduce communications overhead and many past security schemes have suggested that high turnover can be achieved. Several security studies are expected to be trustworthy and less expensive. This paper aims to propose a self-assured approach (SAA) based on trust assessment for secure routing to secure high-security constancy and security for reliable database delivery based probability assessment. The assessment will be evaluated and the node maintains and identifies among random momentary errors and deliberate malicious actions and asses a node total trustworthiness to have secure and safe communication.
Chennai Hydrological Drought Prediction
Authors:   Computer Science and Engineering, SRM IST KTR, Tamil Nadu India., Jat* Priyansha T , Nautiyal Nikhil ,   Computer Science and Engineering, SRM IST KTR, Tamil Nadu India., Pushpalatha Dr. M. ,   Computer Science and Engineering, SRM IST KTR, Tamil Nadu India.
Published: May 2020

Chennai also referred to as Madras is located on the Coromandel Coast off the Bay of Bengal. It's the largest cultural, economic and academic center of south India. The city faced an acute water shortage in June 2019. Chennai was entirely captivated with spring water resources to fulfill its water needs. There are four reservoirs within the city, namely, Red Hills, Cholavaram, Poondi and Chembarambakkam, with a combined capacity of 11,057 mcft. These are the main sources of water for the town. Apart from the reservoirs, alternate sources of fresh water are Minjur and Nemelli desalination plants; Cauvery water from Veeranam lake; aquifers in Minjur, Panchetty and Neyveli; Here is an initiative to place together a dataset that has the data about the varied water sources available within the city using LSTM (Long Short Term Memory), TensorFlow, NumPy, Keras and ANN (Artificial Neural Network). The idea is to figure out whether we can use this dataset to: 1. Visualize the water need / usage of the town 2. Identify whether the water sources availability is going to be able to meet the wants till the following monsoon? 3. How bad is the current water crisis compared to previous years?
Multifunctional Rendering Swarm Robot
Authors:   Department of Mechatronics & Robotics, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur (Andhra Pradesh), India., kumar* Inapanuri. Aditya Hemanth , Jagadeesh Dr. Anne ,   Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur (Andhra Pradesh) India., Praveen Malla ,   CEO at Robocoupler Techno Solutions, Visakhapatnam (Andhra Pradesh), India., Brijesh Isukapatala ,   Department of computer science and engineering, Gayatri vidya parishad , Visakhapatnam (Andhra Pradesh), India.
Published: May 2020

Multifunctional Rendering Swarm robot make a huge impact in this present busy world . This robot works perfectly as an surveillance robot as well as an personal assisting robot for household purposes and most of the work that carried on is swarm mechanism of the personal assisting robots. This have dual mechanism for moment such as mecanum wheel as well as Klann mechanism to the six legs to overcome the hard terrain landscapes in particular house surroundings , with and arm mechanism to pick and place of assigned objects. This robot can be controlled by voice as well as limited hand gestures to perform tasks which use text to speech and speech to text, Convolution Neural Networking (CNN) respectively . This robot have a 3600 night vision camera for surveillance at night times through OpenCV . This will be doing parallel jobs such as talking to you as an assistant , cleaning house , and many more . as the advancement in the world is not up-to the real time applications, this helps in interfacing future and present situations to get updated. As swarm robots, this will be updating its status to the fellow robots and assigning particular task to divide and do faster which depends on deep learning and artificial intelligence. All this can be acknowledged and examined by any nominee of the user who are at distant places in case of any emergency and respond as a speech modulation talk back , digital display available for commands display or any work been processed, time consuming smart work is been done through this robot. This is also equipped with the sanitizer for the people hygiene and safety. This robots can also be used in military, schools, software industries and many more industries.
Visualization of Real-time Twitter Data based on Sentiment Classification
Authors:   CSE, V R Siddhartha Engineering College, Vijayawada, India., Peeka* Susmitha , Tabassum Sabiya ,   CSE, V R Siddhartha Engineering College, Vijayawada, India., Pallem Dhanushya ,   CSE, V R Siddhartha Engineering College, Vijayawada, India., Aravapalli Sowmya ,   CSE, V R Siddhartha Engineering College, Vijayawada, India., Sobhana Dr. M. ,   Sr. Assistant Professor, CSE, V R Siddhartha Engineering College, Vijayawada, India.
Published: May 2020

Analyzing information from social media sites could bring great challenges and opportunities to solve many real time problems. It gives the public opinion about almost every product, personality or any service. The data from social networking sites is more accurate and useful to analyze the public sentiment about the trending topics. The activity of analyzing opinions, sentiments and also the subjectivity of data that is provided, is called sentiment analysis. Tweepy is an easy-to-use python library which is used to extract source data from twitter. From these tweets, features are extracted and then classified using Naïve Bayes algorithm to identify sentiment. This aims to provide an interactive automatic system which predicts the sentiment of the tweets posted in social media using python in real-time. These applications of sentiment analysis are broad and they tend to be very useful in today’s lifestyle. It will evaluate people's sentiment about the trends, entertainment, political issues and products which helps to improve marketing strategies with the help of hashtags, keywords etc.
Design of Agricultural Robot for Fruit Picking and Tuning
Authors:   Student of Mechanical Engineering Department, Koneru Lakshmaiah Education Foundation, Guntur, India., Krishna* Ghanta Vamsi , Rao Dr. Seeram Srinivasa ,   Professor of Mechanical Engineering Department, Koneru Lakshmaiah Education Foundation, Guntur, India., Neerav Bolisetty Venkata ,   Student of Mechanical Engineering Department, Koneru Lakshmaiah Education Foundation, Guntur, India., Satyasai Gattupali E.N.M.S. ,   Student of Mechanical Engineering Department, Koneru Lakshmaiah Education Foundation, Guntur, India., Yaswanth Vangavolu ,   Student of Mechanical Engineering Department, Koneru Lakshmaiah Education Foundation, Guntur, India.
Published: May 2020

There are mainly five steps in agriculture that are plowing, seeding, watering, tuning, harvesting. In this project, we are going to mainly focus on Boring, where a robot with the help of the drilling tool attached at its rear side will be used to remove the soil for the plantation. Secondly comes plantation, here the person will carry out plantation at desired places. Then comes the maintenance of the crop in which the amount of watering, sunlight, pesticide, fertilizer required for the healthy growth of the plant is controlled by the robot with help of electronic circuits present inside a robot. Then we focus on tuning operation, this module will be taken care of by cutters attached to the robot. Finally, fruit picking operation is done by a three-hand jaw attached to a robot which will be used to pick the fruits. Another additional module is a sample of soil that will be taken and processed with different chemicals and give us output as deficient nutrients in the soil and fertilizers to be added to make soil fertile land and suitable atmospheric conditions for the crop
Design and Analysis of Low Power 4T Sense Amplifier with Capacitive Offset Correction
Authors:   Professor, Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur, India, Dhamodaran* M. , Prema R. ,   PG scholar, Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur, India
Published: May 2020

In this paper Sense Amplifier is analyzed the basic and fundamental operational block in the Static Random Access Memory. The function of the sense amplifier is to amplify the small signal bit line voltages into high voltages. In the existing literature survey, there are many methods available for designing the sense amplifier. In this paper, the cross-coupled sense amplifier is modified into a 4T based sense amplifier. The proposed scheme also developed for capacitive offset correction based sense amplifier. The existing and proposed designs of SA are briefly examined in this paper. The proposed design is implemented in the linear predictive technology model. The parameters like power and energy. The proposed scheme shows the better results compared to the existing method.
Simulative Analysis of Various Mitigation Schemes in WDM Optical Network
Authors:   Department of Electronics and Communication Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, India, Singh* Manjit , Sappal Amandeep Singh ,   Department of Electronics and Communication Engineering, Punjabi University Patiala India
Published: May 2020

The growth of optical systems is stimulated for high channel capacity and high bandwidth to meet the explosive development in data communication. Most of the voice and data traffic is transmitted through optical fiber link in these days. In order to share the large bandwidth of optical fiber by multiple channels of different wavelength with higher data rate WDM networks are used. The performance WDM networks are highly effected by the nonlinear dispersion which needs to be managed. In this paper the performance of WDM optical network is analyzed by using Dispersion compensation fiber, fiber bragg grating, and polarization controller. It has been concluded from the results that the use of polarization controller provides the high quality output as compare to Dispersion compensation fiber, fiber bragg grating for each channel.
A Device for Detecting the Food Freshness.
Authors:   Computer Science and Engineering, GITAM University, Bengaluru, India., M* Manogna , Sai Rachana N ,   Computer Science and Engineering, GITAM University, Bengaluru, India., P Nikita ,   Computer Science and Engineering, GITAM University, Bengaluru, India., N Nishanth ,   Computer Science and Engineering, GITAM University, Bengaluru, India., Sinha Priyanka ,   Assistant Professor Computer Science and Engineering, GITAM University, Bengaluru, India.
Published: May 2020

Food is the most vital thing for every human being living on earth and also plays an significant role in our lives. All the same many diseases are caused by the ingestion of unhealthy and rotten food. India is a developing nation consisting of 1.37 billion people. The ingestion of safe food is the most important requirement.This paper explains the use of different sensors for detecting the food quality. This device uses a temperature and humidity sensor along with pH, gas sensors. The Blynk program helps with knowing the values gathered from sensors. This sensor system can also be implemented in common households, and food consumption made easier using the Blynk application.
AVISAR – An Automated Framework for Test Case Selection & Prioritization using GA for OOS.
Authors:   Professor and Director of Directorate of Jan Shikshan and Extension, J.R.N. Rajasthan Vidyapeeth (Deemed University) , India., Mandot* Prof. Manju , Vats Mr. Prashant ,   Ph.D. in CSE from Banas anasthali Vidyapith, Rajasthan, India.
Published: May 2020

In this paper we have presented an automated unified approach called AVISAR for the testing of the Object-oriented Systems (OOS) by Test Case Prioritization (TCP) & their selection using Genetic Algorithm for the OOS. The testing of OOS has become a more challenging task as nowadays it has been widely accepted as a paradigm for large-scale system designing. In this research paper we have also studied the Genetic algorithms in relation to their applications for providing solutions to the various aspects of the OO Testing. As a result after implementing the tool AVISAR using GA’s it has proven to be useful in providing effective solutions to resolve the issues related to the OO Testing domain. Thus it can be used for reducing the efforts of the users for testing by efficient selection of effective test cases.
Fire-Fly Algorithm Based ANN Classification of Power Transformer Faults
Authors:   Assistant Professor, Department of EEE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, India., Supriya* P.Lakshmi , Reddy P.Ram Kishore Kumar ,   Professor, Department of EEE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, India.
Published: May 2020

Monitoring and estimating the states of the transformer during faulted phase condition is essential to continuity of supply. Varied techniques are proposed for faulted phase detection to improve condition assessment. In this paper, we propose a novel method to detect and classify power transformer faults using wavelet transform Multi Resolution Analysis (MRA) as feature extracted parameter vector and Fire-Fly Algorithm (FFA) based Artificial Neural network training as classification method. The observed Dissolved Gas Analysis (DGA) waveform data is analyzed with wavelet transforms (WT) to identify abnormalities which is supported by MRA. In MRA, the current, voltage and temperature of winding and oil are decomposed into high and low frequency components. The magnitude of components, signifies the feature vector, gives a detection criteria. After detecting feature vector, dominant coefficients of WT can be used to train the ANN with FFA based learning algorithm. Different types of faults are created on transformer such as Single Line-Ground (SLG), Line-Line (LL), Double Line-Ground LLG, Three phase fault (LLLG) for the analysis using WT and ANN. The detection and classification of the fault signal are executed and examined in different winding location and different fault conditions. Finally, the presented precise model recognizes the faults based on performance metrics with high classification accuracy for various classes.
Fuzzy Logic Inference System for Identification and Prevention of Coronavirus (COVID-19)
Authors:   Associate Professor, Department of Mathematics, Chaudhary Charan Singh University, Meerut, India., Sharma* M. K. , Dhiman Nitesh ,   Research Scholar, Department of Mathematics, Chaudhary Charan Singh University, Meerut, India.
Published: May 2020

Now a days Novel Coronavirus named COVID-19 becomes major health concern causing severe health issue in human beings and it becomes a pandemic. It’s a kind of zoonotic that means it can transmit animals to humans. It may spread via polluted hands or metals. No specific treatment is available so far for COVID-19, so initial identification and preventions for COVID-19 will be crucial to control or to break down the chain of COVID-19. For this purpose, we have proposed a fuzzy inference system to diagnose the COVID-19 disease by taking six input factor like as; Ethanol, Atmospheric Temperature (AT), Body Temperature (BT), Breath Shortness (BS), Cough and Cold and the output factor has divided into three linguistic categories which denotes the severity level of the infected patients.
Semantic Image to Image Translation using Machine Learning Algorithms
Authors:   Department of CSE, VR Siddhartha Engineering College, Vijayawada, India, Pranavi* G. , Brunda M.Pranava ,   Department of CSE, VR Siddhartha Engineering College, Vijayawada, India, Jashwanth P. ,   Department of CSE, VR Siddhartha Engineering College, Vijayawada, India, Chaitanya V.Naga ,   Department of CSE, VR Siddhartha Engineering College, Vijayawada, India, Rao D.Rajeswara ,   Department of CSE, VR Siddhartha Engineering College, Vijayawada, India
Published: May 2020

In semantic image-to-image translation, the goal will be to learn mapping between an input image and the output image. A model of semantic image to image translation problem using Cycle GAN algorithm is proposed. Given a set of paired or unpaired images a transformation is learned to translate the input image into the specified domain. The dataset considered is cityscape dataset. In the cityscape dataset, the semantic images are converted into photographic images. Here a Generative Adversarial Network algorithm called Cycle GAN algorithm with cycle consistency loss is used. The cycle GAN algorithm can be used to transform the semantic image into a photographic or real image. The cycle consistency loss compares the real image and the output image of the second generator and gives the loss functions. In this paper, the model shows that by considering more training time we get the accurate results and the image quality will be improved. The model can be used when images from one domain needs to be converted into another domain inorder to obtain high quality of images.
Indian Counterfeit Currency Detection
Authors:   Pursuing, Btech, Computer Science and Engineering at Velagapudi Ramakrishna Siddhartha Engineering College affiliated to JNTUK, Kakinada., Hawanika* Yepuri Spoorthi , Sreeja Kapavarapu Sai ,   pursuing Computer Science program at VR SiddharthaEngineering College affiliated to JNTUK University, Kakinada, Shamitha Yangala ,   Pursuing B.Tech final year in Computer Science and Engineering at VR Siddhartha Engineering College affiliated to JNTUK University, Kakinada., Priyanka Bandaru Yuva ,   Pursuing, B.Tech Computer Science and Engineering at VR Siddhartha Engineering College affiliated to JNTUK University, Kakinada, Srilatha Manam ,   Assistant professor, Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada
Published: May 2020

The counterfeit currency printing rate has been increased with the progress of color printing Technology. Some people are printing fake currency using some laser printers. Therefore, the counterfeit currency notes production instead of the original currency notes has been rapidly increasing. This requires an efficient system that identifies the counterfeit currency note and displays the result. This paper developed a system consisting of image preprocessing, gray-scale conversion, image segmentation, edge detection, feature extraction, and comparison modules. The currency note is scanned and the scanned image is used in the modules. The outcome of the system will foretell if the note is counterfeit or genuine.