bluuebunny/crossref_metadata_2025 huggingface dataset:
Rumour Detection Models & Tools for Social Networking Sites
Authors:   CSE department, Osmania University, Hyderabad, India., Ali Mohammed Mahmood , Qaseem Mohammad S. ,   CSE department, Nawab Shah alam college of engineering, JNTUH, Hyderabad, India., Rahman Ateeq ur ,   CSE department, Shadan College of Engineering and Technology, JNTUH, Hyderabad, India.
Published: January 2020

Efficient utilization of social networking sites (SNS) had reduced communication delays, at the same time increased rumour messages. Subsequently, mischievous people started sharing of rumours via social networking sites for gaining personal benefits. This falsified information (i.e., rumour) creates misconception among the people of society influencing socio-economic losses by disrupting the routine businesses of private and government sectors. Communication of rumour information requires rigorous surveillance, before they become viral through social media platforms. Detecting these rumour words in an early stage from messaging applications needs to be predicted using robust Rumour Detection Models (RDM) and succinct tools. RDM are effectively used in detecting the rumours from social media platforms (Twitter, Linkedln, Instagram, WhatsApp, Weibo sena and others) with the help of bag of words and machine learning approaches to a limited extent. RDM fails in detecting the emerging rumours that contains linguistic words of a specific language during the chatting session. This survey compares the various RDM strategies and Tools that were proposed earlier for identifying the rumour words in social media platforms. It is found that many of earlier RDM make use of Deep learning approaches, Machine learning, Artificial Intelligence, Fuzzy logic technique, Graph theory and Data mining techniques. Finally, an improved RDM model is proposed in Figure 2, efficiency of this proposed RDM models is improved by embedding of Pre-defined rumour rules, WordNet Ontology and NLP/machine learning approach giving the precision rate of 83.33% when compared with other state-of-art systems.
Optimizing Deep Network for Image Classification with Hyper Parameter Tuning
Authors:   Department of Computer Science, Assam University, Silchar,Assam,India., Gogoi Munmi , Begum Shahin Ara ,   Department of Computer Science, Assam University, Silchar, Assam, India.
Published: January 2020

The deep network model comprises of several processing layers and deep learning techniques help us in representing data with diverse levels of abstraction. Based on the practical importance and the efficiency of machine learning, optimization of deep models are carried out relating to the objective functions and its parameters for a particular problem. The present work focuses on an empirical analysis of the performance of stochastic optimization methods with regard to hyperparameters for the deep Convolution Neural Network (CNN) and to understand the rate of convergence of the optimization methods in high dimensional parameter spaces. Experimentation has been carried out in deep CNN model with different optimization methods viz. SGD, AdaGard, AdaDelta and Adam. The empirical results are evaluated using benchmark CIFAR10 and CIFAR100 datasets. The optimal values of the hyperparameters obtained demonstrates that the optimizer Adam shows the best results compared to other methods viz. SGD, AdaGard, and AdaDelta over the considered datasets. Further, it is noted that classification accuracy can be increased by choosing the best optimization techniques with hyperparameter tuning to get the optimal configuration of the deep CNN model.
The Cultural Value and Character Education of Full Day School Implementation in Indonesia
Authors:   Bengkulu University, Bengkulu, Indonesia., Budiman Dwi Aji , Fathurrochman Irwan ,   State Institute of Islamic Studies (IAIN) Curup, Curup, Indonesia., Putra M. Rusni Eka ,   STKIP PGRI, Lubuklinggau, Indonesia., Bahri Syaiful ,   State Institute of Islamic Studies (IAIN) Curup, Curup, Indonesia.
Published: January 2020

This qualitative paper investigated how full-day school is viewed from the aspect of character education and the formation of cultural values for students. The results were obtained that the learning process by extending learning time is an activity in instilling character education through interaction between teacher, student, and the school environment. Character education is carried out through extracurricular activities, carrying out intense and ongoing communication between teachers and students and organizing routine diversity activities. The implementation of the full-day school policy is also an effort to encourage awareness of students through cultural activities as a step to instill concern and love for the values of local traditions in the city of Bengkulu by conducting music, dance and other creative activities.
Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method
Authors:   Assoc. Professor of CSE,KoneruLakshmaiah Education Foundation (Deemed to be University), AP, India., Nayak Rudra Kalyan , Pavitra S.Y.H. ,   Student of 4 th YearB.Tech (CSE), KoneruLakshmaiah Education Foundation (Deemed to be University), AP, India., Tripathy Ramamani ,   Asst. Professor of MCA, United School of Business Management,Odisha, India., Prathyusha K. ,   Student of 4 th YearB.Tech (CSE), KoneruLakshmaiah Education Foundation (Deemed to be University), AP, India.
Published: January 2020

With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).
An Adaptive Slide Window Security Method for Transaction Updation in Data Stream Mining
Authors:   Research Scholar, CSE, Koneru lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist,.A.P.India, Kumar Jayendra , Raju Anitha ,   Associate Professor, CSE, Koneru lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist.,A.P.India
Published: January 2020

Data steam mining has gained large interest in current research domain. Where various information’s are retrieved based on the content of the context, the accuracy of the input stream with respect to its privacy is a major challenge. Windowing technique is used an effective approach in providing security measure in data stream mining. The recent develop windowing approach operates using sliding window, where anonymity is focused by different processing rules. The linear search sliding window has a constraint of search overhead and loss of generality under distributed information. In this paper, a new adaptive window approach for privacy coding in data stream mining is proposed. This presented approach is developed with the concern of minimize the search overhead and accuracy in search mining performance using adaptive window monitoring.
Effect of Epoxy Coating on Structural Steel Section under Tension
Authors:   Associate Professor at the Department of Civil Engineering, Bhilai Institute of Technology, Durg, District Durg, India., Sharma Shraddha , Gupta Mohan Kumar ,   Professor and Head at the Department of Civil Engineering, Bhilai Institute of Technology, Durg, District Durg, India., Jaiswal Shashi Kant ,   Professor at the Department of Civil Engineering, Bhilai Institute of Technology, Durg, District Durg, India., Gupta Smita ,   Assistant Professor at the Department of Structural Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.
Published: January 2020

Structural steel connections are one of the most critical components of any steel structure as the cross-sectional area of steel sections reduces due to bolt holes. Failure of a structural connection may lead to failure of entire steel structure. Many researchers have tried to improve the connections previously by gluing fiber polymers at the connection. In this research glue or epoxy has been used around the bolt holes to simplify the process of using fiber polymers with steel. Epoxy is a combination of resin and hardener. It is proposed here to strengthen the structural steel connection in new structures and also in existing structures by applying a thin coat of suitable epoxy around the bolt hole. Thin steel plate with hole at the centre was tested under tensile load and results for ultimate load, breaking load, corresponding stresses and displacements obtained. Another steel plate with same geometry was then epoxy coated around the hole and same test was conducted on this specimen. Results for yield, ultimate, breaking loads and corresponding stresses and displacements recorded. The load-displacement curve is generated for both the cases and compared. The ultimate load bearing capacity of the plate increased in tension slightly after epoxy coating. Significant increase in breaking load observed as the thickness of epoxy layer was increased. Increase in the ductility of the composite plate is seen as increase in displacement is visible. There was considerable reduction of average stress around the center hole. The results indicate that structures can be safer against total failure and will give adequate warning before collapse.
An Intrusion Detection Model Based on Deep Long Short Term Recurrent Neural Network
Authors:   Research Scholar, Department of Computer Science and Systems Engineering, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, AP, India, Rao K. Narayana , Rao K. Venkata ,   Professor, Department of Computer Science and Systems Engineering, Andhra University College of Engineering (A), and DeanAcademic Affairs, Andhra University, Visakhapatnam, AP, India., PVGD Prasad Reddy ,   Sr. Professor, Department of Computer Science and Systems Engineering, Andhra University College of Engineering (A), and Vice-Chancellor, Andhra University, Visakhapatnam, AP, India.
Published: January 2020

With the rapid increase of network based services and internet users on various platforms are becoming the major targets of attacks. Intrusion detection is the process of monitoring the attacks and analyzing their signs and violation of security policies which are occurring in the systems or networks. Intrusion Detection System is a prominent research area in security analysis and evaluation. In order to identify the attack type, we proposed Deep Long Short Term Memory-Recurrent Neural Network (DLSTM-RNN) method with seven optimizers and 500 epochs to train and test a dataset. Initially the data transformation, normalization are used to preprocess the data. The preprocessed train and test data is given input to the model. The bench mark NSL-KDD dataset used to train and test the model. The results are obtained for five-class classification (attack types).The model outperformed with adamax optimizer on NSL-KDD dataset. The metrics accuracy, detection rate, and false alarm rate areevaluated to ascertain the detection efficacy of the model. We compare the model to existing convolutional learning methods.
Delamination Assessment of FRP Composite Plate Using Natural Frequencies
Authors:   Department of Mechanical Engineering, MVGR College of Engineering(A), Vizianagaram, India., Naidu S Sanyasi , Ratnam Ch ,   Department of Mechanical Engineering, AUCE(A), Andhra University, Visakhapatnam, India.
Published: January 2020

The most frequent failure mode of composite plates is delamination. Deboning between the adjacent layers at a particular region of the multilayered composite plate is consider as delamination. It is due to a defect in the manufacturing practice or can be caused by service time conditions, for example, impact by foreign objects. Overall Stiffness of the composites reduces due to delamination. This paper presents the effect on natural frequency due to the delamination of the fiber-reinforced plastics (FRP) composites. In the finite element method, delamination is implemented as a VCCT or cohesive zone method, but in this article, delamination modeled as an open area in the interface layer in ANSYS ACP (pre).Numerical and experimental modal analysis is performed to delaminate as well as intact composite plates. Extract modal parameters like natural frequencies and mode shapes from the modal analysis. The results of the present paper compare among the existing simulation results and observe that good agreement between them. also, study the variation of frequencies with an increase of delamination. The new proposed modeling of delamination is simple and gives accurate results. This method is also used tostudy the delamination effect in composite plates.
Target Object Tracking with Portable Sensors in Wireless Sensor Network
Authors:   Research Scholar, Sri Venkateswara University Gajraula, Uttar Pradesh-India, Vaquar Musheer , Agarwal Sanjay Kumar ,   Professor, Dolphin (PG) Institute of Biomedical & Natural Sciences, Dehradun, Uttarakhand-India
Published: January 2020

Detection and tracking of moving target objects is one of the important problems of wireless sensor systems. In recent years, portability has become an important research area for the WSN community. Although it was never thought that the WSN arrangement was completely stable, portability was initially perceived as some of the difficulties that must be overcome, including network, inclusion and use of vitality. Target object dictates the accuracy of target objects with which the position of target objects can be estimated. This problem becomes a test, especially given the portability of the sensor and the target object, in which the directions of the sensor and the target object have to be captured. [1, 17] Through this review, we consider that we consider the question of following signals that release appropriate target object using compact sensors that depend on the social issue of the signal. Since the versatile movement of the lens is unclear, the portable sensor controller uses the approximation accumulated by a remote sensor so that the compact article indicates the time of appearance (TOA). [4] The portable sensor controller confirms the TOA estimation information of both compact target object and portable sensors to evaluate their areas before guiding the development of portable sensors to achieve the target object. We propose the calculation of approximation (min-max) to calculate the monitoring area, which can be effectively understood through quasi-different programming (SDP), and apply a cubic potential to the portable sensor path Can do. We measure the area of portable sensors and focus on each other to improve the following accuracy.[2, 5] We determine the characteristic relationship between several basic parameters of the frame and the trackingtarget object, including the thickness of the sensor, the range of detection, the portability of the sensor, and the target object. We examine the relationships and the ability to influence by multiple parameters of the framework and locate the base number of portable sensors that are required to maintain the tracking of target objects in an MSN. To further improve the execution of the framework, we propose a weighted monitoring calculation, using estimation information more efficiently.[3] Our entertainment results suggest that insufficiently insured calculations provide excellent follow-up that can be improved by requesting greatness with an equal number of sensors when contrasted and with the position of static sensors.
An Image Based Encryption Algorithm for Multimedia Applications
Authors:   currently working as Assistant Professor, Karpagam Academy of Higher Education, Coimbatore., Mohanarathinam A. , Subramaniam Kamalraj ,   currently working as Associate Professor, Karpagam Academy of Higher Education, Coimbatore., NB Prakash ,   currently working as Associate Professor, National Engineering College, Kovilpatti., GR Hemalakshmi ,   currently working as Assistant Professor, National Engineering College, Kovilpatti., Venkatesan G.K.D.Prasanna ,   currently working as Dean, Karpagam Academy of Higher Education, Coimbatore.
Published: January 2020

Multimedia is the most popular domain in recent era, where it handles distinct information such as text, image, music, video and etc. The security and channel capacity are the challenging parameters in real time multimedia applications. In this research work, an image based double encryption scheme has proposed along with DCT compression technique. The input image is encrypted by Chaotic Baker map and Advanced Encryption Standard (AES) algorithms. The encrypted image is compressed by DCT compression technique. The PSNR and MSE of proposed double encryption method attains 77.1617 and 0.0013 respectively. The experimental results are compared with the individual encryption methods shows that the performance of Double encryption is superior to the other existing methods.
A Novel Planar Monopole Antenna with Truncated Ground Plane for Wireless Communication
Authors:   Department of ECE, Sri Krishna college of Technology, Coimbatore, India., Vadivelu R , kumar G Santha ,   Department of ECE, Sri Krishna college of Technology, Coimbatore, India.
Published: January 2020

In this paper, triple-band planar monopole Microstrip Antenna intended for different applications like Bluetooth, Wi-Fi, Wireless LAN (2.4 GHz), LTE 2500 band, WiMax (3.5 GHz), and a piece of C-band applications. The reception apparatus has been intended to work at different recurrence groups, for example, 2.25 – 2.5 GHz, 3.32 – 3.97 GHz, and 5.90 – 8.67 GHz individually. The proposed receiving antenna comprises of a planar monopole reception apparatus imprinted on RT/duroid 5880 substrate (through a general permittivity of 2.2 and loss tangent of 0.0009) and the base side printed with a truncated ground. The planar monopole reception apparatus has been viably structured and reproduced by utilizing Ansys-HFSS design tool. The component of the proposed receiving antenna is 40 × 28.4 × 1.575 mm3. The reenacted outcome shows return loss, voltage standing wave ratio (VSWR), radiation pattern, and gain of the Antenna. Also, the truncated ground plane structure is straightforward, vigorous and possesses little space, building it appropriate for different applications.
Adaptive Ability and Stability of the Genotypes of Collection Samples of Green Beans in Siberia
Authors:   Novosibirsk State Agrarian University, Novosibirsk, Russia., Yakubenko O.E. , Parkina O.V. ,   Novosibirsk State Agrarian University, Novosibirsk, Russia., Andreeva Z.V. ,   Novosibirsk State Agrarian University, Novosibirsk, Russia., Chepurnov G.Yu. ,   Novosibirsk State Agrarian University, Novosibirsk, Russia.
Published: January 2020

The interaction of the genotype with individual groups of factors has long been the research subject of plant breeders and geneticists. The phenomenon of the "genotype-environment" interaction accompanies the entire history of plant breeding. Due to the necessity of expanding the area of green beans cultivation and the climatic variability, assessing the gene pool of this crop by its adaptive ability and stability for isolating highly plastic and stable genotypes with high productivity is quite relevant. This task is also important for searching for genotypes resistant to biotic and abiotic stress. The article provides an assessment of the adaptive ability and stability of the following green beans varieties: overall adaptive ability, variance of the specific adaptive ability, relative stability, and breeding value of the genotype. The studies were performed at the experimental field of the Garden of Michurinists Educational and Production Farm of the Novosibirsk State Agricultural University. In 2015 – 2018, the authors studied the adaptive ability and stability of 16 varieties of bushy green beans by the main components of the crop productivity. The adaptive ability, the relative stability, and the selective value of the genotypes were determined by the method of A. V. Krichevsky, according to which the adaptive ability is the ability of a genotype to maintain its inherent phenotypic manifestation of the trait in particular environmental conditions. The overall adaptive ability (OAA) of the genotype characterizes the average value of a trait in various environmental conditions, and the specific adaptive ability (SAA) — a deviation from the OAA in a particular environment. The analyzed parameters were calculated by the yield of green beans based on the results of four years of hybrids cultivation. The comparative analysis of common green bean varieties showed a wide polymorphism in terms of the OAA, the SAA, and the breeding values of the genotypes (BVG). This allows identifying promising genotypes to be included in the selection process by certain characteristics.
Face Spoofing Detection using Enhanced Local Binary Pattern
Authors:   Pursuing Master’s degree, Electronics and Communication Engineering from National Institute of Technical Teacher’s Training and Research, Chandigarh, India, Grover Karuna , Mehra Dr. Rajesh ,   Head of Curriculum Development Center National Institute of Technical Teacher Training & Research, Chandigarh, India.
Published: January 2020

Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synthetic forged of the original biometric trait to the sensing objects. In order to guard face spoofing, several anti-spoofing methods are developed to do liveliness detection. Various techniquesfordetection of spoofing make the use of LBP i.e. local binary patterns that make the difference to symbolise handcrafted texture features from images, whereas, recent researches have shown that deep features are more robust in comparison to the former one. In this paper, a proper countermeasure in opposite to attacks that are on face spoofing are relied on CNN i.e. Convolutional Neural Network. In this novel approach, deep texture features from images are extracted by integrating the modified version of LBP descriptor (Gene LBP net) to a CNN. Experimental results are obtained on NUAA spoofing database which defines that these deep neural network surpass most of the state-of-the-art techniques, showing good outcomes in context to finding out the criminal attacks.
Choke Point in Mini Expansion Device for the Development of Portable Vaccine Kit
Authors:   a professor of Mechanical Engineering Dept, Xavier University, Cagayan de Oro City, Philippines., Dollera Elmer B. , Dablo Christine France C. ,   a registered Mechanical Engineer and a graduate of Mechanical Engineering Department, Xavier University, Cagayan de Oro City, Philippines., Dollera Godofredo B. ,   Jr., a registered medical doctor of Camiguin General Hospital, Mambajao, Camiguin Province, Philippines., Nasara Hermes O. ,   a registered Mechanical Engineer and a graduate of Mechanical Engineering Department, Xavier University, Cagayan de Oro City, Philippines., Maquiso Kyle Bryan A. ,   a registered Mechanical Engineer and a graduate of Mechanical Engineering Department, Xavier University, Cagayan de Oro City, Philippines,
Published: January 2020

Vapor compression refrigeration system is substantial to human comfort and needs that contributes to the progress mainly in agriculture, food preservation and in medical application. One of the applications that this study focused on is the study of the choke points in mini expansion device for the development of portable vaccine carrier kit. This study utilized a vapor compression refrigeration system, and aims to improve the system operation of a small-scale vapor compression refrigeration system by using spirally-arranged capillary tubes with five(5) different hydraulic diameters, namely; 0.20mm, 0.25mm, 0.30mm, 0.35mm and 0.40mm. A 1/8 horsepower vapor compression refrigeration system of a water dispenser is used as an experimental rig that supply the required refrigerant flow on the spirally- arranged expansion device. Guitar strings are used to reduce the hydraulic diameter of the commercially available capillary tubes. With the inserted guitar strings, the five(5) different hydraulic diameters in this study would be connected to the experimental rig. The data gathering method is developed by using a data logger and the fabricated spirally-arranged capillary tubes connected to the experimental rig. The determination of its lengths of choke point and the behavior of the pressure drop is measured during the different trials used for every hydraulic diameter of the spirally-arranged capillary tube. The mathematical equation that correlates the hydraulic diameters of the capillary tubes and their corresponding choke points is represented by the equation y = -1,836.0x2 + 2,319.0x-1 - 1.7860, where y is the distance of the capillary tube choke point and x is the hydraulic diameter of the fabricated spirally-arranged capillary tubes. This equation correlates the hydraulic diameter of a capillary tube to its corresponding choke point length with a value of R2 = 0.9947.
Behaviour of Exoskeleton Structures under Wind Loading
Authors:   Assistant Professor, Department of Civil Engineering, R.M.K. Engineering College, Kavaraipettai (Tamil nadu) India., G. Dhilip Kumar R. , M. Divya ,   Research Scholar, Department of Civil Engineering, National Institute of Technology, Trichy (Tamil nadu) India.
Published: January 2020

Tall buildings structural system has evolved a lot in recent times. Nowadays, a new system is being in the building as two parts namely exterior system and interior core. The exterior system will be located in the perimeter of the structure whereas the inner core located at the center which mainly accounts for the transfer of gravity load and the exterior system takes care of lateral loads rather than gravity load. The exterior shell is nothing but a diagonal grid which are effective as both gravity and lateral support to the tall buildings and this structural system is named as Exoskeleton. This exoskeleton diagrid structures imparts greater stiffness and lateral stability to the slender tall buildings. The objective of this study is to obtain a shape efficient lateral load resisting exoskeletal system using diagrid elements. Hence, the steel exoskeleton structures are analysed separately for both wind and seismic loads using ETABS 2015. 72-story structure with plan area of 2500 m2 adopted with different base plan (octagonal, rectangular and triangular shapes) with aspect ratio less than 5 has analyzed and designed. Gust factor method is adopted for wind loading since the tall structure undergoes vibration for various acceleration of the wind. The Gusts factor various from 1.19 to 2.11 in the design. Several analytical studies were conducted for different base plan with various diagrid angles and then this paper focus on obtaining a shape effective structure with optimal diagrid angles say 55.5, 65.7, 77.8 degree under adverse loading condition. Moreover, this work is done by considering following parameters say interstory drift, shear absorbing performance, the behaviour of inner core and outer shell.
Enhancement of Shear Strength Parameters of Clayey Soil by using Polypropylene Fiber
Authors:   M. Tech Scholar Department of Civil Enginering, RIMT University ,Punjab, India., Qayoom Faisal , Sachar Anuj ,   Assistant Professor, Department of Civil Engineering, RIMT University, Punjab, India., Kaushal Manish ,   Assistant Professor, Department Of Civil Engineering, RIMT University. Punjab, India
Published: January 2020

When the foundation soil is not strong enough to hold on with the loads transmitted to it .At such places the structure could be damaged, in order to eliminate this threat the soil properties such as stability, shear strength, durability etc are enhanced by mixing it with different admixtures such as lime, cement etc .At some occasions fibers (obtained from waste materials)are used to enhance the soil properties. In past a large number of studies have shown the effectiveness of such mixing materials at considerable percentages. It has been studied that the outcome of such materials when used in soil improvement techniques is handsome. In this examination the polypropylene fiber ,obtained from waste materials has been used. The different percentages of fiber reinforcement are used and the corresponding changes in the shear strength parameters are examined. The effects of polypropylene fiber reinforcement on the soil samples are examined by carrying out Direct Shear Test and Unconfined Compression Test.
Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern
Authors:   Research Scholar, Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli, India., Priyadharshini P. , Zoraida B. S. E. ,   Assistant Professor, Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli, India.
Published: January 2020

Cancer detecting technology plays a vital role in the medical community. Researches have shown that patients that are affected by cancer carry same type of genetic patterns in their DNA. With this in mind, this research work concentrates on analysing gene pattern for detecting cancer using deep learning algorithms. The Feedback based Adaptive Recurrent Neural Network (FA-RNN) approach is designed to classify and analyse the gene pattern recognition. The data augmentation is done to improve the quality of the input data from COSMIC dataset which includes the detection of missing values, removing the noise present in input using multiple imputations and reducing higher base value can be done using dimensionality reduction process. After obtaining the improved dataset, the training phase begins by estimating the exact weight value of feedback layer using feedback weight loop calculation technique to lessen number of repetition during training. Moreover, the error calculation is done to evaluate the exact weight values of feedback layer used for classification. Finally the classification is done by selecting the next appropriate hidden neuron using the neuron selection activation function. The performance of the Feedback based Adaptive Recurrent Neural Network technique can be analysed using the evaluation metrics accuracy, computation time and Root Mean Square Error (RMSE) and the attained results are compared with the Recursive Neural Network(RNN) and Convolutional Neural Network(CNN) algorithms. The obtained results such as higher accuracy, reduced RMSE and less computation time in Feedback based Adaptive Recurrent Neural Network indicates that it performs the enhanced operation than CNN and RNN.
Homomorphic Cryptosystems for Data Security in Cloud Storage
Authors:   Ph.D Scholar, Department of Computer Engineering, PAHERU Udaipur, Rajasthan, India., Devmane Vidyullata , Lande B. K. ,   Professor, Department of Electronics, Datta Meghe College of Engineering, Airoli, Navi Mumbai, India., Hiran Dilendra ,   Professor, Pacific Academy Higher Education Research University, Udaipur, Rajasthan, India., Joglekar Jyoti ,   Professor, Department of Computer Engineering, K. J. Somaiya College of Engineering, Mumbai, India
Published: January 2020

Cloud Computing enables users to use remote resources thus reduces the burden on local storage. However, the use of such services gives rise to new set of problems. The users have no control over the data which they have stored on those storages so to achieve data authentication with confidentiality is utmost important. As every user may not have that expertise so they can request for data verification task to Trusted Verifier (TV) which will be an authorized party to check the intactness of outsourced data. Since the data owner stores the data on the cloud in an encrypted format, it becomes difficult to check the integrity of the data without decrypting. But by using homomorphic encryption schemes the integrity checking can be made possible without original copy. In this paper, we have given implementation and performance details of two homomorphic encryption schemes, Rivest Shamir Adleman (RSA) and Paillier. The RSA is multiplicative homomorphic scheme where the Paillier is additive homomorphic scheme. Both the algorithms are partially homomorphic thus limited in their functions. Due to homomorphic property of these algorithms, original contents will not get revealed in the verification process. This framework will achieve authentication of data by maintaining confidentiality.
Pattern Recognition in Digital Images using Fractals
Authors:   Ph.D., Department of Computer Science and Engineering, Shri Venkateshwara University, Kashmir, India., Farooq Mansoor , Khan Mubashir Hassan ,   Assistant Professor, Department of Computer Applications, Govt. College for Women M. A., Kashmir, India.
Published: January 2020

Pattern recognition in digital images is a conjoint problem with application in remote sensing, electron microscopy, medical imaging and astrophysics, still no general solution which can be rivalled with the human cognitive system in which a pattern can be conceded subject to random positioning and scale. This research has stemmed in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. It allows both ANNs and correlation to be used subject to appropriate pre-and post-processing techniques for digital image processing.
Bio-medical Image Retrieval using Various Statistical Methods
Authors:   University Visvesvaraya College of Engineering, Bengaluru, India,, N Vinutha , S Sandeep ,   Practo Technologies Private Limited, Bengaluru, India., Shenoy P Deepa ,   University Visvesvaraya College of Engineering, Bengaluru, India., R Venugopal K ,   Bangalore University, Bengaluru, India.
Published: January 2020

In recent, the healthcare sectors rely more on imaging technologies for early detection and diagnosis of the disease. But, the abundant images obtained from these imaging technologies have complex disease patterns associated with them and thus an expert requires more time to analyze and arrive at the decision. Hence, the image retrieval techniques have a significant role to assist the experts by retrieving the most similar images existing in the database and also help them to compare a new scan of the patient with the top matched images and arrive at the quick decision during the diagnosis of a patient. So, we have performed our studies on the two-dimensional structural Magnetic Resonance Imaging of the Open Access Series of Imaging Studies dataset. The collected images are preprocessed and categorized into different groups based on the ventricular region of the brain. After the categorization, we employ second and higher-order statistical approaches to extract the textural features. Then the computed textural features of the images existing in the dataset are compared with the textural features of a query image to retrieve the top matched images using similarity distance as the metric. Then the image retrieval performances of the proposed hybrid based statistical methods are measured. The obtained results shows that the combined features of Gray Level Co-occurrence Matrix and Law’s Texture Energy Measure attains the highest precision across the categorized groups of a dataset and it achieves 80% precision for Group1, Group2 images and 60% precision for Group3 images.
Multimodal Transpose Rotation Mobius Transformation Based Cancellable Template Generation Technique
Authors:   Principal, Kamalam college of Arts And Science,Anthiyur,Udumalpet,Tamilnadu,India., Kanagalakshmi Dr.K. , Lakshmipriya Mrs.K. ,   Research Scholar, PG & Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamilnadu, India.
Published: January 2020

Cancelable biometric is such a template security conspire, that replaces a biometric template when the stored template is taken or lost. It is a feature level area transformation where a misshaped variant of a biometric template is produced and coordinated in the transformed space. The issue persevere with the utilization of unique template can be abstained from utilizing cancellable biometrics. In this work, a nonexclusive structure has been intended for producing irreversible portrayal of templates of multimodal which depends on Mobius transformation on unique picture. So the template security is additionally improved. The simulation output of the proposed framework give better execution in Identification of clients. Another strategy called "MTRMT" is proposed to address the issue of stored Templates. The Proposed epic strategy has been assessed with the ongoing fingerprint got from 50 volunteers of veerapandi village in Coimbatore locale. The exploratory outcome shows the better execution of the proposed framework.
Different Injection Pressure on VCR Engine using Hibiscus Oil
Authors:   Associate Professor, Department of Mechanical Engineering, AarupadaiVeedu Institute of Technology, Vinayaka Mission’s Research Foundation, babu K. Surendra , Michael B. Samuel ,   Research scholar, Department of Mechanical Engineering, AarupadaiVeedu Institute of Technology, VinayakaMission’s Research Foundation, chandran Anandhu ,   UG Sholar, Department of Mechanical Engineering, AarupadaiVeedu Institute of Technology, Vinayaka Mission’s Research Foundation., Moncy Jaimin ,   UG Sholar, Department of Mechanical Engineering, AarupadaiVeedu Institute of Technology, Vinayaka Mission’s Research Foundation., Mon Muflin ,   UG Sholar, Department of Mechanical Engineering, AarupadaiVeedu Institute of Technology, Vinayaka Mission’s Research Foundation.
Published: January 2020

This present examination researches the presentation and outflow qualities distinctive infusion pressure on factor pressure proportion of a diesel motor utilizing Hibiscus oil. With different mixes of hibiscus seed oil, biodiesel and diesel mixes are set up to use as fuel on factor pressure proportion diesel motor. The outcomes indicated that decrease in brake warm effectiveness, nitric oxide and increment in brake explicit fuel utilization, carbon monoxide, hydrocarbon with Blends of hibiscus seed biodiesel mixes than flawless diesel. The minor departure from execution parameters like Brake explicit fuel utilization, Brake warm effectiveness and NO emanations Hydro carbon, Carbon Monoxide are surveyed and broke down.
Design of a Hexagonal Solar Fish Dryer
Authors:   Associate Professor, Mechanical Engg., Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation, Chennai. India, Lakshminarayanan N. , Mahesh R. ,   Assistant Professor Grade II, Mechanical Department, Aarupadai Veedu Institute Of Technology, Vinayaka Mission’s Research Foundation, Chennai. India., Amalraj R. ,   UG Student, Dept. of Mechanical Engg, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation, Chennai. India., P Mithun.N. ,   UG Student, Dept. of Mechanical Engg, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation, Chennai. India., Nair Jayakumar ,   UG Student, Dept. of Mechanical Engg, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation, Chennai. India.
Published: January 2020

Solar drying is a general methodology adopted to protect agricultural goods, fishes as well as meat in tropical as well as subtropical areas. Drying helps to maintain the quality of product and improve its shelf-life by bringing down the wastage to minimum levels. Conventional driers have lot of limitations like infestation by insect, rodents, other animals, exposure to wind-borne dirt and dust, lack of proper monitoring as well as escalated cost of the automatic dryers. Solar dryers are being used all around the world in different countries in varied models. These dryers are of two different methods one that work with natural convection with air-flow established by buoyancy and the other with air flow by a fan in forced convection mode. This is a paper on the outcome of a solar dryer designed especially for fisherman to dry fishes effectively. A different configuration with an objective to capture major amount of incident solar energy and dry fishes at a faster rate is being suggested as an alternative.
RF Energy Harvesting using Wireless Sensor Network for Low Power Applications
Authors:   Associate Professor, Department of ECE, SCSVMV, Kanchipuram, India., Umapathy K. , Muthukumaran D. ,   Assistant Professor, Department of ECE, SCSVMV, Kanchipuram, India., Chandramohan S. ,   Assistant Professor, Department of ECE, SCSVMV, Kanchipuram-631561.
Published: January 2020

The evolution of telecommunications systems emphasizes the importance of RF energy in the surroundings. This energy can be utilized for less power device namely wireless sensor network. The performance of small DC/RF converter is calculated in this paper in sequence to enslave the WSN performance for energy recovered. Specially to attain the performance in GSM band the efficient rectifier is achieved. Based on the selection of rectifying diode the design methodology works and causes the losses in rectifying antenna. By employing the slope method the advantageous performance is obtained in Advanced Design System (ADS) software. Implementing Schottky diodes in rectifier and voltage doubler HSMS 2850 is used. RF/DC conversion the maximum efficiency is 38% is achieved with 10dBm input power level. A uniform distribution of sensor node with network loads is utilized to control the WSN performance of Base station location as well as distance. For such reason Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is utilized.
Systolic Fir Filter using Bypass Multiplier
Authors:   Assistant Professor, Dept of ECE , Guru Nanak Institute Of Technology, Khanapur, Telangana, India., Swarnalatha E. , Hariveena Ch. ,   Assistant Professor, Dept of ECE, CMR Engineering College, Kandlakoya, Medchal, Telangana, India., Vuppala Saritha ,   Assistant Professor, Dept of ECE Teegala Krishna Reddy Engineering College, Meerpet Medbowli, Hyderabad, Telangana, India.
Published: January 2020

In DSP the most common function is Finite Impulse Response (FIR) filter which is realized in field Programmable gate Arrays (FPGAs). For efficient Very Large Scale Integration (VLSI) computation systolic FIR filter architecture has attractive models. High speed is the major concern for fast computation in real time Digital Signal Processing (DSP) applications. In conventional systolic FIR filter method uses general array multiplier structure which takes more time to compute the process with high design complexity with less power. To overcome this problem the systolic FIR filter utilizing Bypass Feed Direct Multiplier(BFDM) is proposed. The proposed method 16 tap systolic FIR parallel processing offers less delay with less design complexity which is used in image and signal processing applications. The proposed method is simulated using Xilinx ISE 12.4 ISE tool and the functions are evaluated by MODELSIM 6.3C.
Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor
Authors:   Research Scholar, Department of ECE, KLEF, Vijayawada, Andhra Pradesh, India., Venkatapathi P. , Khan Habibulla ,   Professor, Department of ECE, KLEF, Andhra Pradesh, India., Rao S. Srinivasa ,   Professor & HOD, Department of ECE, MRCET, Hyderabad, Telangana, India.
Published: January 2020

Cognitive radio (CR) is a new technology that is proposed to improve spectrum efficiency by allowing unlicensed secondary users to access the licensed frequency bands without interfering with the licensed primary users. As there are several methods available for spectrum sensing, the energy detection (ED) is more popular due to its simple implementation. However, ED is more vulnerable to the noise uncertainty so for that reason, we present a robust detector using signal to noise ratio (SNR) with dynamic threshold energy detection technique is combined with the kernel principal component analysis (KPCA) in Cognitive Radio Networks (CRN). The primary purpose of kernel function is to ensure that its dependency relies on inner-product of data without the feature space data requirement. In this paper, with the aid of kernel function the spectrum sensing with the leading eigenvector approach is modified to a feature space of higher dimensionality.By introducing of efficient detection system with dynamic threshold facility helps the better detection levels even low SNR values with quite a lot of noise uncertainty levels. The simulation results of the proposed system reveal that KPCA outperforms with that of traditional PCA in terms of false alarm rate, detector performance when tested under various uncertainties for orthogonal frequency division multiplexing signal.
Modern with Advanced Direction in Green Cloud
Authors:   Assistant Professor, Department of Computer Science & Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India., Jain Jaishree
Published: January 2020

As the Multiple Clouds or Cloud are very flexible and very vast field of data with the correspondence advantages. But as the traffic loads are increasing day by day, which directly hits on the speed of cloud computing. Hence, Green Cloud is added with cloud computing to maintain the traffic load balancing, Cloud computing is introduced with (ICTs) to face advance challenges for the cloud platform and general securities. Cloud computing advancements have an assortment of use spaces, since we are getting solid versatility, reliability and superiority at general minimal efforts. The Cloud computing upheaval is upgrading current systems administration like Cloud computing implement it’s on tool and merge to take the advantages of Green cloud, and also offering promising natural security prospects just as monetary and innovative favorable circumstances. These advances can possibly improve vitality productivity and to decrease carbon impressions and e-squander. Cloud computing can be changed by this type of highlights into green Cloud computing. After deep study of Green Cloud, It has been founded that Green Cloud should be audit by the primary accomplishments. I have worked in the paper to turn in the modern direction to improve the old research, so that cloud computing take more or latest advantagesby the use ofgreen cloud, and the clients can work smoothly without facing said issue.