bluuebunny/crossref_metadata_2025 huggingface dataset:
Vectorization and Optimization of User Behavior Data in E Learning Systems
Authors:   GlobalLab, LLC, Moscow, Russia., Tatiana Krupa
Published: March 2020

At the first stage, an applied scientific research developed a procedure for collecting data on the parameters of user interaction with the user interface. This input procedure receives many heterogeneous messages about the actions of a particular user in the interface, while the output represents a vector that describes the user in aggregated form. The set of vectors for different users, in turn, was then used as input for the k-means clustering algorithm, the result of which is the user's attitude to one of the k clusters that distinguish the user by the type of behavior. User interface interaction data is available to 67.8% of GlobalLab platform users. There is no such data for the Diary.ru electronic diary. Considering that not all users of the GlobalLab platform took measures to create a project, ideas, work with questionnaires and educational materials, the total number of students for whom the value of all 4 variables differs from the neutral one was 9.7 thousand.
Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images
Authors:   Research Scholar, Dept. of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamilnadu, India., Joseph Nisha , Murugan D. ,   Prof & Head, Dept. of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India., Thomas Basil John ,   Assistant Professor, Dept. of Business Administration, Sur University College, Sultanate of Oman., A Ramya ,   Assistant Professor, B.S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.
Published: March 2020

This paper introduces a scheme for retrieving deep features to carry out the procedure of recognising brain tumors from MR image. Initially, the MR brain image is denoised through the Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) after that the contrast of the image is improved through Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the pre-processing task is completed, the next phase is to extract the feature. In order to acquire the features of pre-processed images, this article offers a feature extraction technique named Deep Weber Dominant Local Order Based Feature Generator (DWDLOBFG). Once the deep features are retrieved, the next stage is to separate the brain tumor. Improved Convolution Neural Network (ICNN) is used to achieve this procedure. To explore the efficiency of deep feature extraction and in-depth machine learning methods, four performance indicators were used: Sensitivity (SEN), Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). The investigational outputs illustrated that the DWDLOBFG and ICNN achieve best outputs than existing techniques.
An Energy Efficient Hybrid PSO Algorithm in Cloud Environment
Authors:   Assistant Professor, Department of Computer Science, Khalsa College for Women, Amritsar, Punjab, India., Khera Ranjandeep Kaur , Banga V. K. ,   Principal and Professor, Department of Electronics & Communication Engineering, Amritsar College of Engineering and Technology, Amritsar, Punjab, India.
Published: March 2020

Algorithms exist to schedule various tasks in real time cloud environment. Nowadays many researchers are trying to schedule heavily loaded situations in real time cloud environment using swarming technique. For such studies many parameters need to be considered like cost of the system, processor latency, number of tasks and so on. With the increase in the number of tasks in the set, processing time also increases. In this situation, processor latency is at peak as the number of tasks increases and system costs increase. So the above mentioned problem is handled by proposing a task scheduler that uses a PSO algorithm to remove the limitations of past studies in a heavily loaded situation. The Particle Swarm optimization (PSO) and Invasive Weed Optimization (IWO) are combined to propose a new technique called the HWO algorithm. The proposed algorithm is recommended for preventive tasks in the single-processor in real-time environment systems.
A Privacy Preserving Upload Model for Crowd Sourced Health Care Monitoring System
Authors:   Research Scholar Department of CSE,MUIT, Lucknow, India., Farooqui Umar Khalid , Bharti Ajay Kumar ,   Professor2, Department of CSE,MUIT, Lucknow, India
Published: March 2020

In recent years governments become more concern about health care monitoring which cannot be accomplished without enhancing trust on underlying system as various citizens hesitate to upload their sample because of privacy reasons and obviously the governmental decisions are based on the data collected by various PHCs and third party medical agencies. The accuracy and authenticity of this third party owned data is always doubtful. Crowd sourcing(a collaborative framework) make its sound presence in development of large scale health projects Scientist also impressed from crowd sourcing which is a faster and better alternative to traditional methods for predicting and monitoring infectious diseases. However the success of this type of crowd sourcing depends on the trust on underlying system as the user is always looking firm commitment to preserve their privacy and win a promise of not being re-identified later. Here in this work we suggest a privacy protecting framework for upload process which could fulfill user's diverse privacy requirements while guaranteeing the quality of health care data.
Upgraded Service Oriented Architecture for Smart Injection Molding.
Authors:   M.Tech Student, Mechanical Department, Sachdeva institute of technology, Farah, Mathura, Uttar Pradesh, India, Babloo  , Singh Devendra ,   Assistant Professor, Mechanical Department, Sachdeva institute of technology, Farah, Mathura, Uttar Pradesh, India., sharma Ajay Kumar ,   Assistant Professor, Mechanical Department, Institute of Engineering and technology, Lucknow, Uttar
Published: March 2020

This paper investigates current advances towards Smart Injection Molding, and presents the idea of Smart Molds or Molds 4.0. It exhibits a contextual analysis of the assembling of a medicinal gadget, including the test set-up of an instrumented shape, just as general suggestions on the most proficient method to actualize a Smart Manufacturing vision in the plastic Industry 4.0. Moreover, it incorporates a proposition of an Advanced Cyber-Physical System (CPS) Service Oriented Architecture (SOA) for constant observing and information examination of a shrewd microinjection trim procedure and for brilliant molds instrumentation as an approach to acknowledge such savvy vision.
Stages of a Mathematical Model Developing of the Educational Trajectory
Authors:   GlobalLab, LLC, Moscow, Russia., Tatiana Krupa
Published: March 2020

The model is based on the use of interactive teaching methods. A characteristic feature of the use of interactive technologies is the organization of training that takes into account the inclusion of all the students of a group without exception in the learning process. Joint activity means that each participant makes his or her own individual contribution, whereby in the course of work there is an exchange of knowledge, ideas, and methods of activity. An environment of educational communication is created that is characterized by openness, interaction of participants, equality of their arguments, accumulation of common knowledge, and the possibility of mutual evaluation and control. The use of neural networks to study and predict educational assets will provide research and development organizations and teams with innovative and effective ways of conducting research in the field of educational theory, modeling of the cognitive processes related to formation of different student competencies, and devising more appropriate methods for estimating student educational outcomes.
Identification Features of External and Internal Variables in the Mathematical Model of Educational Trajectory
Authors:   GlobalLab, LLC, Moscow, Russia., Tatiana Krupa
Published: March 2020

The article describes the main stages of identification and formation of variables which are included in the developed mathematical model of the student’s educational trajectory. Analyzed are such groups of external variables as geographical location, age and gender, learning style and parameters of user interaction with the user interface, academic performance, level of possession of complex skills. The numerical values of the variables in different conditions are determined. The types of sessions in which information is collected are highlighted. Data arrays from such information sources as the GlobalLab online platform and the electronic diary Diary.ru were analyzed. Using the studied variables, a mathematical model of the student is presented, which takes into account many properties. The aim of the project is to create a technological model for the application of machine learning methods to predict the optimal educational trajectory of the student. Achieving this goal and using the scientific and technical results of the Project will provide a number of useful technical, technological and technical and economic effects.
Solving Component Identification Problem by using Evolutionary Method
Authors:   Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Allahabad, India., Pandey Anil Kumar , Pandey Tulika ,   Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Allahabad, India.
Published: March 2020

Today system is day by day increasing and the maintenance of a system is a very difficult task. To explain the component identification problem for architecture which is support to a component based technology. To identify component and optimal , first we describe the Ant clustering approach and then we explain the method how the ABC helps for groups for cluster the Usecase in this paper proposes a .ABC is a population based NP type problem with heuristic that can be used to find approximate solutions to difficult combinatorial optimization problems. This paper presents an effective ABC approach with ant colony in this paper we also find the optimal number of components or clusters. Finally the ABC applies into different exiting system.
Secured Cloud Data Transmission using Cross ABE Algorithm
Authors:   Department of Computer Science, Research Scholar, Joseph Arts and Science College, Thirunavalur, India., Chockalingam K. , Velmurugan L. ,   Department of Computer Science, Assistant Professor, Joseph Arts & Science College, Thirunavalur , India.
Published: March 2020

Cloud security is becoming more essential than ever with the tremendous development of delicate cloud data. The cloud information and services are located in massively scalable data centers and can be accessed anywhere. Unfortunately, the development of cloud users has been followed by an increase in cloud malicious activity. More and more vulnerabilities are being found, and fresh safety advisories are being released almost every day. Millions of customers surf the cloud for different reasons, so they need extremely secure and persistent services. The cloud storage system interconnect with the a load of potential security risks. So the cross encryption of Ciphertext Policy Attribute Based Encryption (CPAB) and Key Policy Attribute-based encryption algorithm which increases the security level in the encryption side. A segmentation part helps in splitting the encrypted file in storing the data in the cloud side, the Desegmentation part in the receiver side can easily combines spitted data into the single file for validation examine an authentication level in the received data. Here the cloud storage easily with the file fragmentation processes. This processes research over the storing mass amount of data on off- site installation, which can eliminate the cost in maintaining the physical hardware. Cloud's future includes a much greater degree of privacy and authentication, particularly in extending the variety of apps. We suggest a straightforward data protection model where data is encrypted before it is introduced in the cloud using key policy attribute-based encryption to ensure data confidentiality and safety. The storing data is the most common application for the cloud server.
Improvement of Network Utility and Energy Efficiency in DSME based Internet of Things Networks
Authors:   Research Scholar, ECE, Career Point University, Kota, Rajasthan,India, Chebrolu Phaneendra Kumar , Kumar Bandi Kiran ,   Research Supervisor, ECE, Career Point University, Kota, Rajasthan, India
Published: March 2020

This paper provides a comparison between IEEE 802.11 and IEEE 802.15.4e standards in the context of Internet of Things (IoT). These emerging standards are the amendments of IEEE 802.11 and IEEE 802.15.4 to support IoT based applications. The 802.11 has a channel access scheme, Distributed coordination function (DCF). On the other hand, IEEE 802.15.4e introduces five MAC behavior mode. Among these five modes, DSME is well suited for IoT. A comparison between these two standards is discussed in this paper by using an analytical model and are validated through ns-3 simulations. Results show that the DSME show significant improvement in the performance of DSME when compared to the legacy IEEE 802.11 DCF.
How Blockchain Make Better the Supply Chain in the Automotive Industry
Authors:   Laboratory of SEISEE, ENSAM of Casablanca Morocco, Yahiaoui Sanae , Fedouaki Fayçal ,   Industrial Engineering Department, ENSAM of Casablanca Morocco., Mouchtachi Ahmed ,   Director of ENSAM of Casablanca Morocco.
Published: March 2020

The traditional SC is a model that meets the supply expectations of intermediaries. There are gaps in this model, such as relationships between members of the supply chain or consumers' ignorance of information about the source of the products. This article presents a new SC model with Blockchain. To do this, we carried out a comparison between the traditional supply chain and the new chain with the Blockchain in order to designate a model that meets our needs. This BKC method is a new technology that offers effective and confident solutions in businesses. This new model reinforces the reliability of the system based on transparency and traceability in all chain operations, assets and financial means and removes many of the disadvantages of the traditional supply chain. In addition, the Blockchain coordinates all transactions and multi-agents in the SC
News Story Retrieval Based on Textual Query
Authors:   Computer Engineering Department, Gujarat Technological University, India, Dave Namrata , Holia Mehfuza S. ,   Assistant Professor, Electronics department, Birla Vishwakarma Mahavidyalaya ,India
Published: March 2020

This paper presents news video retrieval using text query for Gujarati language news videos. Due to the fact that Broadcasted Video in India is lacking in metadata information such as closed captioning, transcriptions etc., retrieval of videos based on text data is trivial task for most of the Indian language video. To retrieve specific story based on text query in regional language is the key idea behind our approach. Broadcast video is segmented to get shots representing small news stories. To represent each shot efficiently, key frame extraction using singular value decomposition and rank of matrix is proposed. Text is extracted from keyframes for further indexing data. Next task is to process text using natural language processing steps like tokenization, punctuation and extra symbols removal as well as stemming of words to root words etc. Due to unavailability of stemming and other methods of preprocessing of text in Guajarati language, we have given basic stemming technique to reduce dictionary size for efficient indexing of text data. With proposed system 82.5 percent accuracy is achieved on Gujarati news video dataset ETV.
Effective Selection of Resources for Construction using Program Tool
Authors:   Pursuing M.E Construction Engineering and Management, Department of Civil Engineering, Kongu Engineering College, Erode, Tamilnadu, India, Dhanasekar G. , Sivakumar A. ,   Assistant Professor, Department of Civil Engineering, Kongu Engineering College, Erode, Tamil nadu, India., Madhumitha L. ,   Pursuing M.E Construction Engineering and Management, Department of Civil Engineering, Kongu Engineering College, Erode, Tamilnadu, India, Karthikeyan P. ,   Assistant Professor, Department of Management Studies, Kongu Engineering College, Erode, Tamilnadu, India
Published: March 2020

In the Indian scenario construction industry facing a major problem is cost and time overrun. Effective time performance and cost performance are very important to execute the project in a successful manner by keeping them within the prescribed schedule and cost. Overall cost and duration of construction projects affected by the effective resource selection factor. This paper's objective is to rectify the improper selection of resources by a programming tool. Field survey and codebook study did collect the needed data to feed in the programming tool. The prepared tool gets distributed and making to access by every stakeholder of construction projects. This may result in the selection of construction resources as effectively. The term cost overrun in the resource part will be reduced.
NBC Model for Early Prediction of At-Risk Students in Course
Authors:   Assistant Professor, Department of Computer Science & Engineering, G. Pulla Reddy Engineering College, Kurnool India., Sunanda P. , Kavitha D. ,   Professor, Department of Computer Science & Engineering at G. Pulla Reddy Engineering College, Kurnool India.
Published: March 2020

Increase in computer usage for different purposes in different fields has made the computer important to learn things. Machine learning made systems to learn things and work accordingly on their own. Among the different fields that use machine learning, the education field is one. In the education field, machine learning has led to the advent of a digital-enabled classroom, speech recognition, adaptive learning techniques, and development of artificial instructor. Along with this, the prediction has its importance. In the education field, the main problem is students drop out. The machine learning predictive modeling approach can be used to identify the students who are at-risk and inform the instructor and students before reducing the dropouts. The main intention of this paper is to model a system that could be a solution to reduce the drop-outs and increase the education standards in students by early predicting their risk in a course.
Transfer Learning Based MA Detection TL-MAD
Authors:   Ph. D., Research Scholar ,Department of Computer Science and Application, Gandhigram Rural Institute(Deemed to be University), Gandhigram, India., Devi M. Kalpana , Rani M. Mary Shanthi ,   Assistant Professor, Department of Computer Science and Application, Gandhigram Rural Institute (Deemed to be University), Gandhigram, India.
Published: March 2020

Diabetic Retinopathy (DR) is a microvascular complication of Diabetes that can lead to blindness if it is severe. Microaneurysm (MA) is the initial and main symptom of DR. In this paper, an automatic detection of DR from retinal fundus images of publicly available dataset has been proposed using transfer learning with pre-trained model VGG16 based on Convolutional Neural Network (CNN). Our method achieves improvement in accuracy for MA detection using retinal fundus images in prediction of Diabetic Retinopathy.
Influence of Partial Replacement of Micro-Silica by Fly-Ash on Strength Properties of Reactive Powder Concrete
Authors:   Department of Civil Engineering, Sinhgad College of Engineering, Pune, India., Kulkarni Jidnyasa D. , Bhusari Jyoti P. ,   Department of Civil Engineering, Sinhgad College of Engineering, Pune, India.
Published: March 2020

Reactive powder concrete (RPC) is the ultra-high strength concrete made by cementitious materials like silica fumes, cement etc. The coarse aggregates are completely replaced by quartz sand. Steel fibers which are optional are added to enhance the ductility. Market survey has shown that micro-silica is not so easily available and relatively costly. Therefore an attempt is made to experimentally investigate the reduction of micro-silica content by replacing it with fly-ash and mechanical properties of modified RPC are investigated. Experimental investigations show that compressive strength decreases gradually with addition of the fly ash. With 10 per cent replacement of micro silica, the flexural and tensile strength showed 40 and 46 per cent increase in the respective strength, though the decrease in the compressive strength was observed to be about 20 per cent. For further percentage of replacement, there was substantial drop in compressive, flexural as well as tensile strength. The experimental results thereby indicates that utilisation of fly-ash as a partial replacement to micro silica up to 10 per cent in RPC is feasible and shows quite acceptable mechanical performance with the advantage of utilisation of fly-ash in replacement of micro-silica.
Fractional Order Sliding Mode Controller Fed Load-Frequency Control of Multi-Area Deregulated Power System Network
Authors:   Ph.D Scholar, JNTUA, Ananthpur Andhra Pradesh, India., Kumar K. Vimala , Ganesh V. ,   Professor, JNTUP, Pulivendula, Andhra Pradesh, India.
Published: March 2020

This paper presents a Fractional Order Sliding Mode Controller (FOSMC) for load frequency control of multi area power system in deregulated environment. In deregulated power system the design of controller is more complicated due to contracted and un-contracted load demands. This proposed controller shall take care of system nonlinearities and uncertainties under bilateral contract scenario for sudden load disturbances. The performance of proposed controller compared to PI controller and without any controller.
Hybridization of Bat Algorithm with XGBOOST Model for Precise Prediction of Stock Market Directions
Authors:   Assistant Director, Tamil Virtual Academy, Chennnai, Jeyakarthic M. , Punitha S. ,   Assistant Professor, Department of Computer Science, DGGArts college for Woman
Published: March 2020

In recent days, prediction of stock market returns is generally treated as a forecasting problem. The implicit volatile nature of stock market across the world makes the prediction process highly challenging. As a result, prediction and diffusion modeling undermine a wide range of issues present in the stock market prediction. The minimization in prediction error will greatly minimize the investment risks. This paper presents a new method to determine the direction of stock market variations indicating gain and loss. A new machine learning ML based model is applied to predict the direction of stock market prices. The presented model undergoes preprocessing, feature extraction and classification. Initially, preprocessing takes place using exponential smoothing. Then, required features are extracted from the preprocessed dataset. Afterwards, an effective Bat algorithm (BA) with the XGBoost model called BA-XGB is applied for forecasting the stock prices in market. The proposed model predicts whether the stock values gets increased or decreased based on the price existing n days in advance. The presented model is experimented using Apple (APPL) and Facebook (FB) stocks. The obtained simulation outcome stated that the BA-XGB model has offered superior outcome by achieving a maximum accuracy of 96.42.
Assessing the Rate of Shoreline Changes of Rameswaram Island, Tamilnadu, India, using MATLAB Component Runtime Utility & Digital Shoreline Analysis System
Authors:   Centre for Applied Geology, The Gandhigram Rural Institute – Deemed to be University, Dindigul, Tamil Nadu, India., Pereira Glitson Francis , B Gurugnanam ,   , Centre for Applied Geology, The Gandhigram Rural Institute – Deemed to be University, Dindigul, Tamil Nadu, India., Choudhury Saroj.B. ,   Scientist-NRSC., Goswami Santonu ,   Scientist- National Remote Sensing Centre., S Bairavi ,   Centre for Applied Geology, The Gandhigram Rural Institute – Deemed to be University, Dindigul, Tamil Nadu, India.
Published: March 2020

The Present study highlights the changes in the coastal tract of Rameswaram island located between the geometric location, latitude 9° 10' to 9° 20' N and the longitude 79° 12' to 79° 30' E. Using Geospatial Technology, Long-term changes have noticed from digitized shorelines taken from Landsat imageries and SOI toposheet for the years 1968, 1978, 1988, 1999, 2009 and 2018. Digital Shoreline Analysis System (DSAS) an extension provided by USGS for ArcGIS used to cast transects perpendicular from the baseline, towards the shorelines. High water Line (HTL) has taken as shoreline in this study. The distance between the shorelines has taken from the cast transects plotted from the baseline. For the rate of change calculation, MATLAB component runtime utility for ArcGIS has used. Based on the Linear Regression Rate (LRR) the shore has classified into High Erosion, Low Erosion, Stable and Low Accretion, High Accretion zones. The island has a shoreline length of 78.4 km, and the studies found that 27.83 km (35.41%) of shoreline is accreting nature, 37.90 km (48.21%) of shoreline is stable, and 12.86 km (16.36 %) of shoreline tends to erode. During the field investigation identified that High amount of erosion occurred in the locations near Pamban, Tharavaithopu and Dhanushkodi. Also, Accretion identified over the locations Ayyanthopu, Natarajapuram and Arichumanai tip. Wind action is one of the physical parameters that induced the erosion in some location of this study area.
Investigation of EEG Signal Classification Techniques for Brain Computer Interface
Authors:   Research Scholar, IKG Punjab Technical University Kapurthala, Punjab, India, Ghumman Mandeep Kaur , Singh Satvir ,   Associate Professor, IKG Punjab Technical University Kapurthala, Punjab, India
Published: March 2020

Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.
SAP Production Management Solutions
Authors:   Federal State Budget Educational Institution of Higher Education «K.G. Razumovsky Moscow State University of Technologies and management (the First Cossack University)», Institute of System Automation, Information Technology and Enterprise, Department of Informative Systems and Technologies, Moscow, Russia., Makeeva Oksana , Sartakov Mikhail ,   D. Mendeleev University of Chemical Technology of Russia, Faculty of Engineering Chemistry, Department of Computer Science and Computer Engineering, Moscow, Russia.
Published: March 2020

the article considers the need for ERP-systems in the management structure of a modern organization. In the course of the work, the main features of the ERP system, the specifics of its implementation, are disclosed, as well as the German SAP ERP system are substantively examined. The author draws attention to the SAP module: PP, which implies production management. The article reveals the main differences and capabilities of SAP in the management of a manufacturing company, as well as presents the frequently used functions of the PP module. In addition, the author considered the availability of combining the PP module with other program modules to achieve the most effective managerial control of production processes. In the second part of the article, the differences in the functioning of food production in the market are highlighted and the requirements that the ERP system must meet are controllability, variability, and automation of the management process. In the conclusion of the article, the main problems that the head of the enterprise is faced with when implementing an ERP system are highlighted.
Flexural Motions of Beams on Foundation Subjected to Moving Concentrated Force
Authors:   Senior Lecturer, Department of Mathematics, Ekiti State University, Ado-Ekiti, Nigeria., N. Ogunyebi S. , A. Adedowole ,   Senior Lecturer, Department of Mathematical Sciences, Adekunle, Ajasin University, Akungba-Akoko, Nigeria., O. Ogunlade T. ,   Senior Lecturer, Department of Mathematics, Ekiti State University, Ado-Ekiti, Nigeria, A. Oyedele A. ,   Senior Lecturer, Department of Physics, Ekiti State University, Ado-Ekiti, Nigeria.
Published: March 2020

In this present paper, the dynamic analysis of nonprismastic beams subjected to moving concentrated forces is investigated at constant speed. Two cases of load- beam interaction problems described by the Dirac delta function with constant and harmonic magnitude mobile forces are studied. The technique called Galerkin’s method in conjunction with integral transform method was employed to solve the motion equation. From the numerical results, it is evidently seen that an increase in the foundation stiffness provides reduction on the beam deflection. And furthermore, the issue of resonance is closely monitored and observed to have reached earlier in constant magnitude than harmonic variable magnitude problem. Results presented in this work are useful in constructions engineering designs.
―G‘s-Remedies 1.0 (Directus): Making Analysis Simple‖a Unique Amalgmation of Genetic Disorder Database with Sequence Analysis Tools
Authors:   Research Scholar, Maharshi University of Information technology, Lucknow India., Srivastava Gaurav Kumar , Kumar Santosh ,   Associate Professor and Head, School of computer science, Maharishi university of information technology Lucknow India.
Published: March 2020

Paper 'G’S-REMEDIES 1.0 (DIRECTUS)' is a specially designed web-based software. That software fulfills the elementary requests of a Bioinformatician during the study and analysis of principal sequences. PERL, PERL-CGI, and HTML are used for the designing and development of the DIRECTUS. This special tool can be helpfull for computing various parameters that are helpful in ANALYSIS of NUCLEOTIDE and AMINO ACID RESIDUES, and the READING FRAMES. The G’S-REMEDIES 1.0 (DIRECTUS), tool emanates with the personal unique database on the GENETIC DISORDERS. This unique database gives full details almost any specific genetic disorder and it also gives information about mutation type and genetic disorder affected chromosome. The latest version provides more than fifteen analysis tools and also gives a complete image of more than fifty genetic disorders, and next versions of the tool will comes with databases of viral infection databases such as eye disorders databases, and also improve to PRIMER designing tools, etc. G’S-REMEDIES 1.0 (DIRECTUS) is freely available for academic use.
Nature Inspired Algorithms as a Tool for Diseased Millets Leaf Segmentation
Authors:   Research scholar, Atria Institute of Technology, Bangalore, India, P Pavithra , P Aishwarya ,   Professor, Department of CSE, Atria Institute of Technology, Bangalore, India.
Published: March 2020

Agriculture is the main occupation undertaken by majority of population in India. The Indian farmers produce food grains for entire population. Growing population needs constant increase in quantity and quality of agriculture produce. This is possible only by adopting improved technology in production and plant protection. Hence, technology intervention is at every stage of production and food processing is needed. Timely and suitable plant protection measures directly improve quantity and quality of agriculture produce. Effective result oriented plant protection mainly involves early detection of pest and diseases and suitable control measures. The entire human population consume products of food grains like cereals and millets as their staple food. This paper aims at summarizing common image processing methods to identify plant diseases and how it could be improved using nature inspired algorithm.
Identification and In-silico Prediction of Hermetiaillucens Larval Protein that Inhibits the Progression of Cervical Cancer
Authors:   Research Scholar, Department of Biotechnology, REVA University, Bangalore, Rajasekhar Nisha , N Ramesh ,   Director, Dean-Training, Placement and planning, School of Applied Sciences, REVA University, Bangalore, N. Prashantha C. ,   Assistant Professor, Department of Biotechnology, REVA University, Bangalore
Published: March 2020

The aim of the current research is to assess the anticancer activity of the proteins identified from the crude protein extract derived from Hermetiaillucens larvae by computational methods. Bioinformatics methods were used to identify the protein sequence and virtual screening for the prediction of protein structure, their physico-chemical characteristics and functional aspects, that aid in exploring the anti-cancer interaction that is inclined to inhibit the oncoprotein activity. Due to the pathological process transpired by HPV, an array of genetic alterations, including the overexpression of oncoproteins or the inactivation of tumor suppressor proteins like TP53, BCL2, MDM2, ARF and BAX occur. The altered protein signaling pathways convulse to cervical cancer. In this research, we identified proteins, from the crude extract derived from Hermetiaillucens by LC-MS method. Further Computational screening of the selected proteins were employed to assess the functional units which has anticancer activity. We predicted four proteins Metallothionine, Defensin like precursor 1 protein, Heat shock protein 90 and NADH dehydrogenase and eluted the protein sequence from NCBI GenBank database. These sequences were used to predict physicochemical properties and protein structure prediction. Pharmacophore analysis of the peptide sequences as potential targets for cancer treatment was evaluated. Molecular docking of peptide sequences with target protein structure was carried out. To screen the best active potential molecule for cancer treatment the Molecular dynamics of docked protein-peptide structures were administered. The molecular docking of the peptides with onco-proteins has been predicted and virtually screened based on RMSD values. The resultant protein sequence was evaluated further by amino acid sequencing, extensive scrutiny of the proteins, advanced computational imaging, molecular docking examination and assessed the results for clinical interpretation. To summate, bioinformatics assessment enabled us to identify four key proteins derived from Hermetiaillucens larvae that have strong inhibitory function against proteins that cause cervical cancer.
Assessment Cost Estimation for Component Based Software: A Tool Based Technique
Authors:   Assistant professor, Dept. of Computer science, Mata Gujri Mahila Mahavidyalaya (Auto), Jabalpur (M.P), Jain Rajul
Published: March 2020

Assessment cost of the software component contributes a major part in software cost estimation and it is one of the major cost of the software out of- integration cost i.e. the cost of glue codes, assessment costs and tailoring cost. Many researchers have proposed formulas for evaluating assessment and tailoring costs theoretically. Assessment cost is very often considered to be theoretical cost which involves cost of component selection and composition. According to Moguel Goulao et. al assessment cost for overall component can be measured qualitatively and quantitatively both. He has suggested that qualitative measurement is mostly based on views provided by the experts whereas the quantitative measurement is more subjective and repetitive in nature. Various metrics has been suggested by different authors to quantitatively measure the assessment cost for software components. In this work we applying the metrics on case study of UCRS and developed a tool for evaluating the assessment cost which can be used in calculating the overall cost of the software.
Design and Implementation of UAV Architecture for Delivering Medical Products
Authors:   Department of Electronics and Communication Engineering, National Engineering College, kovilpatti, India., Nagasivaranjani K. , Kavitha V. Muthu ,   Department of Electronics and Communication Engineering, National Engineering College, kovilpatti, India., Prabha V. Divya ,   Department of Electronics and Communication Engineering, National Engineering College, kovilpatti, India., Beaula C. Asha ,   Department of Electronics and Communication Engineering, National Engineering, college, Kovilpatti., Tamil Nadu, India.
Published: March 2020

Unmanned Aerial vehicles (UAVs) likewise regularly alluded to as automatons, and are increasing incredible research intrigue, consideration as a significant future innovation. These vehicles have almost boundless potential essentially because of their high mobility and small scale size, that permits their utilization in different applications. Among numerous kinds of UAVs, the multi-copter UAVs with 4 rotors (quadcopter) are being utilized and used regularly. Automatons are semi-self-governing airplanes that can be controlled and worked remotely. Economically accessible automatons are progressively being utilized in an assortment of uses. It is being used in a variety of applications such as,3-D mapping, disaster monitoring, aerial packet delivery, photography and filming, and very useful in the field of monitoring and surveillance also in unmanned cargo system .Recent innovations made huge growth in this field that made the researchers to develop applications which are useful in the field of medicine to deliver drugs ,blood products in remote areas. A medical drugs is delivered to the person based on the location which was identified through GPS module .This system offers the medicine to be delivered to the right person and at right time.
ML Methods for Crop Yield Prediction and Estimation: An Exploration
Authors:   Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India., Alagurajan M. , Vijayakumaran C. ,   Associate Professor, Department CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
Published: March 2020

Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.
Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce
Authors:   Assistant Professor, Department of CSE, JCT College of Engineering and Technology, Coimbatore, India., Karuppan Sivakumar , Nithya N. S. ,   Associate Professor, Department of CSE, KSR College of Engineering , Tiruchengode, India., Ondimuthu Revathy ,   Assistant Professor, Department of ECE, Hindustan College of Engineering , and Technology, Coimbatore, India.
Published: March 2020

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.
Distribution of Data Dissemination Model using DTN Routing Protocols
Authors:   Research Scholar, Dept. of Computer Science and Engineering, Annamalai University, Annamalai Nagar 608 002.India., Parameswari S. , Kavitha K. ,   Research Superviosr/Assoc. Professor, Dept. of Computer Science and Engineering, Annamalai University, Annamalai Nagar 608 002. India., Elango P. ,   Asst. Professor, Perunthalaivar Kamarajar Institute of Engineering & Technology (PKEIT), Karaikal, India.
Published: March 2020

In this research paper compare the protocol’s performance together with the experimental results of optimal routing using real-life scenarios of vehicles and pedestrians roaming in a city. In this research paper, conduct several simulation comparison experiments(in the NS2 Software) to show the impact of changing buffer capacity, packet lifetime, packet generation rate, and number of nodes on the performance metrics. This research paper is concluded by providing guidelines to develop an efficient DTN routing protocol. To the best of researcher(Parameswari et al.,) knowledge, this work is the first to provide a detailed performance comparison among the diverse collection of DTN routing protocols.
Prediction of Future Electric Energy Consumption using Machine Learning Framework
Authors:   Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Savitribai Phule Pune University, Pune, India, Khan Juveria , Rao Jyoti ,   Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Savitribai Phule Pune University, Pune, India., Patil Pramod ,   Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Savitribai Phule Pune University, Pune, India.
Published: March 2020

In the last few years, the expanding energy utilization has imposed the formation of solutions for saving electricity. Of many solutions, one is generating a power saving policies which is defined as prediction of energy in smart environments. This model is built, based on the idea that the building residences are provided with smart meters to monitor energy consumption and can be managed accordingly. Recent prediction models focuses on performance of the prediction, but for developing a reliable energy system, it is required to predict the demand taking into account different scenarios. In this paper we propose a model for predicting future demand for energy according to different conditions using advanced machine learning framework. In this system we have a projector that builds proper state for a particular condition and using that defined state a future power demand is forecasted by the predictor. The proposed model generates utilization predictions for every 2 hours. Demonstrating the electricity consumption data for 5 years, the proposed system achieves a better performance.
Ant Colonized and Taguchi Parallel Scheduliing with Sequence Independent Setup Time
Authors:   Research Scholar, Department of Mathematics, Government Arts College,Coimbatore, Geetha G. , Hemamalini T. ,   Assistant professor, Department of Mathematics, Government Arts College, Coimbatore-
Published: March 2020

The Job Shop Parallel Machine Scheduling (JSPMS) is a hybrid production system, and hence has received significant attention in the past few years. The JSPMS problem is a rationalization of the traditional job shop scheduling problem in computer science and operation research that permits to process operations on single machine out of a set of possible parallel machines. To maximize the job completion rate and minimize job completion time, a hybrid production system is necessary. With this objective, a novel meta-heuristic method is designed. This paper develops a scheduling method called, Ant Colonized and Taguchi Parallel Operation Scheduling (ACTPOS), for JSPMS, aimed to minimize job completion time and maximize job completion rate. The design of AC-TPOS method involves two different models, namely, Ant Colonized Parallel Machine Selection (ACPMS) model and Taguchi Parallel Operation Scheduling (TPOS) model. In ACPMS model, optimal selection of machine is done via operation being processed by parallel machines using local pheromone updating rule concentrating on the makespan time. In addition, the processing time and sequence-independent setup time are considered. Next, in TPOS model, optimal scheduling of operation is performed using Taguchi method concentrating on the makespan rate. Finally, the test results first show that our algorithm outperforms existing methods in terms of job completion rate, job completion time and computational complexity involved in scheduling operations.
Detection of Phishing Websites using an Efficient Feature-Based Machine Learning Framework
Authors:   Department of Computer Applications from Bharadhidasan University (2000), M.Phil Degree in Computer Science from Periyar University (2007)., Ramalingam V. V. , Yadav Paras ,   Computer Science and Engineering, B.Tech student in SRM Institute of Science and Technology., Srivastava Prakhar ,   Computer Science and Engineering, B.Tech student in SRM Institute of Science and Technology.
Published: March 2020

Phishing is a cyber-attack which is socially engineered to trick naive online users into revealing sensitive information such as user data, login credentials, social security number, banking information etc. Attackers fool the Internet users by posing as a legitimate webpage to retrieve personal information. This can also be done by sending emails posing as reputable companies or businesses. Phishing exploits several vulnerabilities effectively and there is no one solution which protects users from all vulnerabilities. A classification/prediction model is designed based on heuristic features that are extracted from website domain, URL, web protocol, source code to eliminate the drawbacks of existing anti-phishing techniques. In the model we combine some existing solutions such as blacklisting and whitelisting, heuristics and visual-based similarity which provides higher level security. We use the model with different Machine Learning Algorithms, namely Logistic Regression, Decision Trees, K-Nearest Neighbours and Random Forests, and compare the results to find the most efficient machine learning framework.