bluuebunny/crossref_metadata_2025 huggingface dataset:
Reinforcement based Multi-Model Deep Learning Algorithm for Classification
Authors:   Department of CSE, Andhra University, Visakhapatnam India., Sunitha* Thella , Devi Dr. G. Lavanya ,   Department of CSE, Andhra University, Visakhapatnam India.
Published: December 2019

Nowadays researchers are focused on processing the multi-media data for classifying the queries of end users by using search engines. The hybrid combination of a powerful classifier and deep feature extractor are used to develop a robust model, which is performed in a high dimensional space. In this research, a three different types of algorithms are combined to attain a stochastic belief space policy, where these algorithms include generative adversary modelling, maximum entropy Reinforcement Learning (RL) and belief space planning which leads to develop a multi-model classification algorithm. In the simulation framework, different adversarial behaviours are used to minimize the agent's action predictability, which has resulted the proposed method to attain robustness, while comparing with unmodelled adversarial strategies. The proposed reinforcement based Deep Learning (DL) algorithm can be used as multi-model classification purpose. The single neural network algorithm can perform the classification on text data and image data. The RL learns the appropriate belief space policy from the feature extracted information of the text and image data, the belief space policy is generated based on the maximum entropy computation.
Household Level Impact Assessment of MGNREGA Scheme
Authors:   teaches in the Department of Political Science and Development Administration, The Gandhigram Rural Institute – Deemed to be University. Gandhigram, Tamil Nadu, India., Haokip Dr. Sonkhogin , Sivakumar* D. ,   full-time research Scholar in the Department of Political Science and Development Administration, The Gandhigram Rural Institute – Deemed to be University. Gandhigram. Tamil Nadu, India.
Published: December 2019

The Mahatma Gandhi National Rural Employment Guarantee Act 2005 (MGNREGA) has been notified by the Government of India on 7th September 2005 with the primary objective of enhancing the livelihood security of the unskilled labors in the rural areas of the country by providing guaranteed wage employment to every household whose adult members volunteer to do unskilled manual work. The MGNREGA, which is one of the flagship projects of the government, promises 100 days of work per year to the unemployed at a CPI inflation-indexed wage rate. As there is an increase in the disposable income on account of the implementation of the scheme, it is expected that the standard of living and the expenditure pattern of the household covered under the MGNREGA scheme would undergo a tremendous change. As most of the expenditure of the rural households covered under the scheme is supposed to be drastically changed, it is felt that there is a need to study the impact of the scheme on these households. This paper is an extract from a Ph. D Thesis titled Household and Village Level Impact of MGNREGS on Governance at the Grassroots: An Assessment of Gram Panchayats in Tamil Nadu. Submitted to the Gandhigram Rural Institute – Deemed to be University.
Enhancement of Matched Filter Response for Chirp Radar Signals using Signal Recovery Technique
Authors:   Egyptian Academy for Engineering and Advanced Technology (EAE&AT), Cairo, Egypt, Salem* Sameh G.
Published: December 2019

The matched filter is one among many effective techniques used to maximize the signal –to- noise ratio (SNR) of chirp radar signals. Besides this enhancement in SNR, it has a drawback due to sidelobe levels which degrade the filter response. There are many additional techniques which reduce these levels such as window techniques and inverse filter. In this paper, a new approach is utilized to completely cancel the level of matched filter side lobes based on the signal recovery of the compressive sensing (CS) theory. The reconstruction process of CS is based on the CAMP algorithm which applied to the response of the matched filter. The recovered chirp radar signals are achieved with completely side lobes cancellation compared to the traditional side lobe reduction technique based on the widow method. The comparison between the proposed and traditional methods is achieved according to the detection performance using Receiver Operating Characteristic (ROC) curve. Besides the detection performance, a resolution in the range is another comparison aspect between these algorithms.
AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning
Authors:   Associate Prof, Department of Electronics & Instrumentation Engineering, BMS College of Engineering, Bangalore, India, K.Mane* Preethi , Rao K. Narasimha ,   Professor, Department of Electronics & Instrumentation Engineering, BMS College of Engineering, Bangalore, India.
Published: December 2019

Occupancy sensing is one of predominant technology used in various control and context aware systems. The efficiency of such systems is highly corelated with the level of accuracy of the classification model of activity or stage detection of the subject or an occupant. The classification approaches based on computer need to evolve optimally for balancing the computational and time complexity with the classication accuracy using occupancy data acquired from Doppler radar. The proposed study presents a simplified framework that emphasizes on feature extraction technique as a medium to obtain more precision in the process of monitoring occupancy. The proposed logic has been implemented using analytical methodology convention and the extracted feature has been subjected to different forms of frequently used machine learning process with respect to processing time inclusive of training and testing period, efficiency, and accuracy of the proposed system.
Modified Cascaded Inverter using ANFIS Controller with Reduced Number of Switches
Authors:   School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India,, Muralikumar Kola , P* Ponnambalam. ,   School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India,
Published: December 2019

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed to the multilevel inverter to eliminate the Total Harmonic Distortions (THD) with a modified cascaded inverter. This method prohibits the variations presents in the output voltage of the modified cascaded inverter. The work is directed to prove that the importance of ANFIS is integrated learning for alteration of learning content affording to learners desires. The concert of the ANFIS model was calculated by means of standard error quantities which shows the ideal setting needed for better certainty. The MATLAB Simulink performance point out that it is affordable and easy to implement the performance of the ANFIS process. The study of different modified cascaded inverter consisting of five-level, seven-level, and nine- level is carried out for evaluating the THD with ANFIS controller and without ANFIS controller is implemented. The learning outcomes are based on the study of various system settings; it demonstrates the usability of the m-learning application. However, it must be noted that the number of inputs increased being considered by the model increases the system response time of the system.
PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts
Authors:   Research Scholar, Visvesvaraya Technological University, Belagavi, Karnataka, India, R Prabha , K G* Mohan ,   Professor and Head, Department of CSE, Presidency University, Bengaluru, Karnataka, India
Published: December 2019

The Global Positioning System is extensively used in the various context and location service-based applications. Any kind of abnormalities requires an efficient and suitable pre-processing algorithm to be implemented on the data which provides accurate results when used in the application synchronizations. This paper illustrates a framework for various pre-processing techniques applied to the real-time GPS data and its effect on trajectory mapping. The technique used includes Prioritized pattern-based, Savitzky-Golay filtering, outlier elimination, de-trending, and coefficient correlation. The performance assessment of methods discussed in this study is calculated in terms of accuracy with the original and re-created trajectory after the pre-processing and found that the best result is given by moving window method.
The Impact of CSR Practices of MNCS on Dairy Industry in Malaysia
Authors:   Putra Business School, Malaysia, Ali* Rana Mohsin , Afzal Muhammad Irfan ,   Putra Business School, Malaysia, Ahmed Shakeel ,   Putra Business School, Malaysia, Mahomed Anuar Shah Bali ,   Faculty of Economics and Management, University Putra Malaysia, Brohi Noor Ahmed ,   Putra Business School, Malaysia
Published: December 2019

This paper aims to conceptualize the relationship between Corporate Social Responsibility Practices of Multinational Companies and growth of Malaysian dairy Industry. Malaysia is a net food importer, and it has been experiencing an upward trend in dairy consumption. But Malaysia imports 95% of its milk requirements and spend a lot of revenue in importing milk and related products as there is not a considerable number of local firms or companies producing dairy products. Malaysian dairy industry depends heavily on Multinational Companies brands e.g.; Nestle, Dutch Lady, F & N. This study focuses on how dairy Multinational Companies are affecting dairy stakeholders of Malaysia; the producers, and the overall local industry. It also concentrates on the procedures and policies which are designed by these Multinational Companies for serving in host countries, specifically regarding Corporate Social Responsibility. The study deals answer that what factors are restraining Malaysian dairy industry to grow and what big Multinational Companies are earning and what are they giving back to Malaysian dairy industry. The proposed methodology for this research will be content analysis, using secondary data from Corporate Social Responsibility reports and financial statements of these Multinational Companies.
Improved Whale Optimization with Buffer Setup Time
Authors:   MCA, M.Phil, Assistant Prof. & Head of Computer Science, Sri Vasavi College, Erode. (Self Finance Wing) Bharathiar University (Category B),, Anandapadmanabhan* K. , Eshwaramurthy Dr V. P. ,   Asst. Prof. of Computer Science, Komarapalayam Arts And Science College, Komarapalayam.
Published: December 2019

The job shop problem is widespread in industrial applications due to lateness in getting a better solution. Buffer plays an important role in the completion of jobs. Proper utilization of buffer by fixing optimal buffer setup time can lead to optimal completion time so that lateness is minimized. This work introduced Improved Whale Optimization with Buffer Setup time Technique (IWOBS) for setting optimal buffer setup time. Using tabu movements, an optimum solution for the nearest buffer setup time is derived and the genetic algorithm is equipped to find fitness score to gather the best buffer setup time with respect to problem consideration. The proposed IWOBS algorithm is proved to be highly effective compared to other existing algorithms.
An Artificial Neural Network Genetic Algorithm with Shuffled Frog Leap Algorithm for Software Defect Prediction
Authors:   Professor*, Dept. Of IT, St. Martin’s Engineering College, Dhulapally, Secunderabad, India, Rao Dr. S.V.Achuta , Patra Dr.P.Santosh Kumar ,   Principal & Professor, Dept of CSE, St. Martin’s Engineering College, Dhulapally, Secunderabad, India
Published: December 2019

Defect prediction performances are significant to attain quality of the software and to understand previous errors. In this work, for assessing the classification accuracy, precision, and recall and F measure for various classifiers are used. The artificial neural network optimizations make the assumption that more than two algorithms for one optimization have been implemented. The optimization makes use of a heuristic for choosing the best of the algorithms for being applied in a particular situation. An approach of hybrid optimization for designing of the linkages method and is used for the dimensional synthesis of the mechanism. The ANN models are assisted in their convergence towards a global minimum by the multi-directional search algorithm that is incorporated in the GA. The results have shown an accuracy of classification of the NN-hybrid shuffled from algorithm to perform better by about 5.94% than that of the fuzzy classifiers and by about 3.59% of the NN-Lm training and by about 1.42% of the NN-shuffled frog algorithm..
Water-Saving Irrigation Technology as a Way of using Water Resources Sustainably in the Khorezm Oasis
Authors:   Department of Irrigation and Melioration, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), Tashkent, Uzbekistan., Khamidov Mukhamadkhan , Matyakubov Bakhtiyar ,   Department of Irrigation and Melioration, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), Tashkent, Uzbekistan., Isabaev* Kasimbek ,   Department of Irrigation and Melioration, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), Tashkent, Uzbekistan.
Published: December 2019

Agricultural water is a limiting resource in many semi-arid countries of the world due to low precipitation and high evapotranspiration. Installation of water-saving technologies has been implemented in many parts of the world to use water more sustainably. The use of water-saving irrigation technology for cotton-grain crop rotation has not been adequately studied in the Khorezm region of Uzbekistan. This study provides insights into such technology used on irrigated meadow-alluvial soils of the Khorezm region with a groundwater level of 2-3 m. On the basis of the irrigation regime, experimental work was carried out in the cotton field to determine the sustainability of irrigation technology. In addition, it was determined the duration of irrigation and the soil moisture pattern along the length of the furrows with a field moisture capacity of the soil 70-80-60%. The results indicate that regardless of the soil physical properties (light, medium and heavy), applying irrigation for cotton using counter furrows showed the highest yield of raw cotton, i.e. 4.1-4.2 ton/ha, which is 1.1-1.2 ton/ha more compared to traditional furrow irrigation. Moreover, under variable-jet furrow irrigation the yields of raw cotton was amounted at 4-4.1 ton/ha, which is about 0.9-1 ton/ha more compared to traditional method of irrigation. At the same time, about 16-18% of irrigation water was conserved.
Existence of Long Memory Phenomenon in Air Pollutant Concentrations using Surrogate Data
Authors:   Department of Engineering Education (DEEd), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia., Zainuri* Nuryazmin Ahmat , Razali Noorhelyna ,   Department of Engineering Education (DEEd), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia, Othman Haliza ,   Department of Engineering Education (DEEd), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia, Jedi Alias ,   Department of Mechanical Engineering and Manufacturing, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia., Zulkarnain Noraishikin ,   Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
Published: December 2019

This study investigated the existence of long memory phenomenon in air pollutant concentrations specifically the ozone concentration obtained at six monitoring stations in Peninsular Malaysia. The main objective was to select the best method in detecting the long memory. Four methods used in this study were Rescaled Range (R/S) method, Aggregated Variance (V/S) method, Aggregated Absolute Value (A/S) method and Peng’s (P/S) method. Surrogate data testing was used to verify the existence of long memory. The average estimated Hurst value obtained by using VS and PS method are found to be near to the actual Hurst value compared to RS and AS method. The biased and MSE value also showed that the VS and PS method is the most appropriate method in estimating the H value. Based on the result obtained, it can be concluded that long memory exists in ozone concentration data used in the study. The VS and PS method are the best method in detecting the long memory phenomenon.
Robust and Efficient Person Re-Identification Model using K-Nearest Neighbor Graph
Authors:   Research Scholar, Department of ISE, Dr. AIT, H J* Vidyarani , Math Dr. Shrishail ,   Professor, Department Of CSE, SKIT
Published: December 2019

the state-of-art person re-identification (prid) models for ranking generally depends on labeled pairwise feature sets information to learn a task-dependent distance metric. Further, in retrieval process, re-ranking is an important mechanism for enhancing the accuracy. However, very limited work is carried out for designing a re-ranking method, particularly for automatic and unsupervised strategies. The existing re-ranking based prid model is not efficient when multiple persons appears simultaneously in second camera. This is because the existing model identify person in second camera by matching the feature sets with feature sets in first camera, individually with respect to other person in the second camera. For overcoming research problem, this paper present robust and efficient prid (reprid) model. First, present a robust learning/ranking method using k-nearest neighbor (knn) graph. Then, this work present a re-ranking method to improve accuracy of prid by using information of co-occurrence persons for matching and reorganizing given rank lists. Experiment are conducted on standard dataset shows robustness and effectiveness of proposed prid method.
Fractional Order Controller for Non-linear Current Control Technique
Authors:   Research Scholar, School of Electrical Engineering, KIIT University, India., Khuntia Ganesh Prasad , Swain Dr. Sarat Chandra ,   Professor, School of Electrical Engineering, KIIT University, India., Dash* Dr. Ritesh ,   Asso. Professor, Christian College of Engineering & Technology, Bhilai, India.
Published: December 2019

linear and Non-Linear controller shows their best performance under different power quality conditions. However, under sub transient condition these controller shows sluggish performance. This paper introduces the application of Fractional order [FO] controller in real world power system phenomena. FO-controller based on proportional integral control action was used here for grid interconnection of inverter. Different power quality issues such as Real & Reactive Power exchange between renewable source and grid have been presented here. Matlab Simulink model was developed for a real-world grid connected model in order to check the robustness of the controller. A comparison between the PI controller and FO-PI controller was presented in this paper.
Principle Component Analysis Based Data Mining for Contingency Analysis on IEEE 30 Bus Power System
Authors:   Research Scholar, Jain University, Asso. Prof. Dept of EEE,AIT, Bangalore, India., M* Lekshmi , Nagaraj Dr.M S ,   HOD Dept. of EEE,BIET Davanagare, India.
Published: December 2019

This Paper is an attempt to develop a Data Mining tool for the contingency of the power system. By mining the big data in the power system and analyzing the early detection of the contingency in the power system a larger cost cutting can be planned. As Mining would reduce the computational complexity of the contingency analysis this attempt would lead to reduction in the hardware use. This paper uses Multiclass Relevance Vector Machine(MCRVM) and Multiclass Support vector machine(MCSVM) in order to mine the data which include the voltage, power generated , power angles , power demand in different lines of the power system. The Data mining would need a data transformation technique, which would reduce the dimensionality of the data introduced for mining. The combination of Data cleansing and the Principal Component Analysis would act as the data transformation technique in this paper. A Matlab based simulation is carried using the IEEE 30 bus system for the contingency analysis by incorporating the loading risk assessment strategy using the Multiclass SVM and RVM and the results are compared and the outputs are tabulated. Active power performance index and the reactive power performance index are used in contingency analysis of the IEEE 30 bus system thus used and the accuracy of classification and the speed of classification with the different methods and the contingency rankings are found and displayed.
Internet of Things: Quality of Services of RABBITMQ & KAFKA
Authors:   Research Scholar, Computer Science & Engineering, Presidency University, Bengaluru, India., Pathak* Abhishek , Kalaiarasan Dr. C. ,   Associate Dean-SOE, Presidency University, Bengaluru, India.
Published: December 2019

Diverse use of various heterogeneous devices in Internet of Things is providing challenges and several issues in Quality of services related to Internet of Things. This paper provides the overview of Internet of Things (IoT) and standards. Factors affecting the quality of services in communication. Standards used for communication in Internet of Things (IoT) are discussed and reviews related to it is presented. RabbitMQ implementation of AMQP as well popular standard like Kafka is discussed. Quantitative and Qualitative differences between RabbitMQ and Kafka is presented. We have also discussed similarities and dissimilarities of Message Queue Telemetry Transport (MQTT) and Advanced Message Queuing Protocol (AMQP) protocols. The main objective of this review is to provide critical review of a functional view of IoT architecture, protocols and standards.
Spatio-Temporal Trends and Variability of Temperature Over Bale Zone, Ethiopia
Authors:   Madda Walabu University, Engineering College, Department of Water Resources and Irrigation Engineering, Bale Robe, Ethiopia., Tolosa* Dereje , Tilahun Fitsum ,   Madda Walabu University, college of Agriculture and natural resource, department of Plant science, Bale Robe, Ethiopia.
Published: December 2019

Understanding of temperature trends and their spatiotemporal variability has great significances on making deep insight for planners, managers, professionals and decision makers of water resources and agriculture. Therefore, this research was set with aim to analyze spatiotemporal variability of temperature and their time series trends over Bale Zone. Statistical analysis: Parametric test with regression analysis on the anomalies like deviation from mean and Non-parametric test with Mann-Kendall test together with Sen’s Slope Estimator & Zs statistics has been used for estimation of trends of a historical data series of monthly, seasonal and annually maximum and minimum temperature of selected meteorological stations in Bale Zone. Both tests relatively shows same results for monthly, seasonal and yearly temperature series. The coefficient of variation (CV) was used for variability analysis. Arc GIS 9.3 software was also used to investigate the spatial variability temperature (minimum and maximum) for the period under review. These methodology has shown a significant increasing and decreasing trends at 95% confidence level for certain time scale temperature series: temperature trends (i.e the mean maximum temperature series) showed a significant increasing trend in Robe (Annual, Spring, February, March, April, May, July, and October), Ginir (February, July, September, and December).Mean minimum temperature series showed a substantial increasing trend in Robe (May, July, September, and November) and Hunte (September). It is also observed that Mean seasonal and annually minimum temperature of the stations have shown higher variability than those mean seasonal and annual maximum temperature of the stations.
Lightweight Security and Privacy Safeguard strategies for Smart Grid management in Client-side Networks
Authors:   B Tech, Students Department Of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India, Sri* R.Satya , Laditya U. ,   B Tech, Students Department Of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India., Ram D. Raja ,   B Tech, Students Department Of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India, Kiran K.V.D. ,   Professor, Department Of Computer Science & Engineering Koneru Lakshmaiah Education Foundation, Vaddeswaram, Ap, India
Published: December 2019

In order to improve the reliability and efficiency of the power grid, the smart grid uses different communication technologies. Smart grid allows bidirectional flow of electricity and information, about the state of the network and the preconditions of the clients, between the different parts of the network. Therefore, it reduces energy losses and generates and distributes electricity efficiently. Although smart grid improves the quality of network services, due to the nature of the power grid communication networks are exposed to cybersecurity threats along with the other threats. For example, electricity consumption messages sent by consumers to the utility through the wireless network can be captured, modified or reproduced by adversaries. As a consequence, the important challenges in smart grid seems to be security and privacy concerns. The smart grid update creates three main communication architectures: the first is communication between the utility companies and customers through diverse networks; that is, Local Area Networks (HAN), Construction Area Networks (BAN) and Neighboring Area Networks (NAN), we refer to these networks as client-side networks in our thesis. The second architecture is the communication through the vehicle-to-network (V2G) connection between the Electric Vehicles and the network to charge or discharge their batteries. The hindmost network is connection of the network with measurement units that extend throughout the network in order to monitor the status and send reports periodically to the main CC to estimate the status and detect erroneous data. The proposed schemes are promising solutions for the security and privacy problems of the three main communication networks in smart grid. The novelty of these proposed schemes is not only because they are robust and efficient security solutions, but also due to their lightweight communication and computing overhead, which qualifies them to be applicable in devices with limited capacity in the network. Therefore, this work is considered an important progress towards a more reliable and authentic intelligent network.
A Novel and Integrated Architecture for Securing Communication in IoT
Authors:   Associate Professor, Department of Computer Science & Engineering, Dr. AIT, Bengaluru, India., Patil* Shamshekhar S. , Biradar ArunBiradar ,   Professor & Head, Department of Computer Science Engineering, East West Institute of Technology, Bengaluru, India.
Published: December 2019

Although the IoT opens the door to endless possibilities, but it is also associated with many risks because all devices connected to the internet involve the use of data points. Therefore, it is essential to ensure IoT security and privacy. A review of existing research works highlights the usage of traditional security scheme based on cryptography for data transmission among IoT nodes and gateways. The proposed system proposes an integrated model that combines lightweight encryption technique with robust and cost-efficient authentication mechanism. The proposed system introduces digital signature-based authentication and complexity minimization in order to resist the involvement of any kind of unknown attacks. The simulation outcome of this model exhibits reliable security, faster response times and energy savings for IoT nodes.
Role of Cultural and Religious Drivers for Ecosystem Change
Authors:   Assistant Professor Department of Architecture, SPAVijaywada, Rao* Dr.Prashanti , Singh Dr. I.P. ,   Associate Professor, Architecture Department National Institute of Technology, Hamirpur.
Published: December 2019

Human societies are chiefly propelled by their unique culture and religious practices. An integral part of the Indian culture is working in unity with nature. This reflection is seen in a various sacred and religious beliefs, arts and crafts, folk tales, traditional practices, rituals, and in the day to day lives of the people of India from the ancient times. Nature Conservation is largely embedded in the ecosystem of Indian civilizations; which in turn is largely exhibited/displayed by their distinctive culture and religious practices. A strong credence that through thoughtful integration between communities and nature efficient ecosystem management is accomplished. Communities have always adapted themselves to better fit into the changing societal needs and thus reshaped their spatial setting. The human species, while safeguarded against environmental proximities through their unique culture and cultural practices, skill, and technology, are eventually wholly dependent on the flow of ecosystem services. The challenge faced by the present global environment is that certain traditional practices lead to downgrading of the ecosystem, while some of them play an appropriate role in advancing biodiversity and preserving the resources, thus serving societies to address the effects of climate change. India with a diverse culture of different communities satisfies the regulatory and functional aspects of nature and natural systems. This paper is intended to look into the religious and traditional aspects of Indian societies to comprehend the dynamism of cultural drivers for ecosystem change and gauge their interrelation with human behavior. The varied case studies have been reviewed by using the Millennium Ecosystem Assessment tool with an objective, that the environment can be protected by changing community behavior. These are examined by using arguments related to values, beliefs, and traditional customary laws associated with different communities across India. The analysis involves the finding of most influencing and commonly influencing factors of values among the communities by calculating and comparing mean and standard deviation for all kind of values associated with varied case study. The study reveals novel ways of integrating conventional, ecological and native wisdom into management processes of the ecosystem. The outcome of the study focuses on policy formulation through participative planning in the decision-making procedure for ecosystem management.
Method for Measuring the Semantic-similarity of Textual Document and Web-pages
Authors:   Software Developer at Uztelecom, Tashkent University of Information Technologies (TUIT), Tashkent,Uzbekistan., Atadjanov* Jasur , Atadjanov Bobur ,   Software Developer at Uztelecom, Tashkent University of Information Technologies (TUIT), Tashkent, Uzbekistan., Abdulazizov Shakhboz ,   Software Developer at Uztelecom, Stock Company, Bachelor of Science in Computer Science and Software Engineering from Inha University in Tashkent (IUT)., Makhmanov Orif ,   Deputy Director at the Center of Introduction and Development of Information and Communication Technologies, Tashkent, Uzbekistan.
Published: December 2019

In this article, the process of semantic comparison of web pages in the global network is addressed. Comparing process is done based on set of words in text. Google Ajax Search and Yandex-API services are used to search and find web pages in global network. The local database is used for the determination of synonyms of the words. For the experiment, we took a document from Global network and its text was exchanged to synonyms and generated new document. The generated document compared with the given algorithm and it showed 90-95% similarity. The developed system with this algorithm used in Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi for verifying students Graduation Qualification works for plagiarism.
Efficient and Convenient Application to Determine the Functions and Analysis of the Reliability of the Device
Authors:   Ph.D. Studentof Information communications technology Department, Bukhara Engineering Technological Institute, Bukhara, Uzbekistan, Ziyodullayevich* Aslonov Qodir , Babakulovich Zhalilov Rashid ,   Associate Professor at the Department ofEnergy, Bukhara Engineering Technological Institute, Bukhara, Uzbekistan, Bakhodirovna Mukhamadieva Zarina ,   Ph.D. Studentof Information communications technology Department, Bukhara Engineering Technological Institute, Bukhara, Uzbekistan, Bekmurodovich Safarov Alisher ,   Ph.D. Studentof Energy auditDepartment, Bukhara Engineering Technological Institute, Bukhara, Uzbekistan, Akif-ogli Mamedov Rasul ,   Ph.D. Studentof Energy department, Bukhara Engineering Technological Institute, Bukhara, Uzbekistan
Published: December 2019

The article describes the types of distribution laws that can be used to determine the reliability of the device (element), analyzed the most frequently used methods for calculating the reliability. When calculating the reliability functions, the modern programming language "Swift" is used and their algorithms are described.
Social Network Users and Social Graph Construction
Authors:   Perm National Research Polytechnic University, Perm, Russia., Plotnikov* Andrei V.
Published: December 2019

The work is devoted to the analysis of friendly relations of the VK social network users. The work aims to obtain connected components of the social graph of the social network users, where edges represent friendships between users and nodes represent users. The total population is approximately 54,000 users (intersection of audiences from two professional communities in the field of social media marketing). The following libraries are used in the work: NumPy and Pandas. The author uses a structural approach focusing on the geometric shape of the network. As a result, a group of 168 users with intra-group connections was allocated from the sample of 1,000 users, of which eight users had visited VK 15 or more days before and eight users had visited the VK from 5 to 15 days before.
Affect Analysis of Multilingual Tweets for Predicting Voting Behavior
Authors:   Computer Science and Engineering Department, Institute of Technology, Nirma University, India, Gohil* Lata , Patel Dharmendra ,   Smt. Chandaben Mohanbhai Patel Institute of Computer Applications (CMPICA), CHARUSAT, Changa, India
Published: December 2019

Social media has been proved as wild card for its role in election campaign across the globe. It has been used for general election of India in year 2014 and year 2019 by political parties for election campaign. Thus social media provides opportunity for electoral prediction. Users from India use regional languages in addition to English language on social media. Multilingual data likely to give better prediction compared to single language data. Affect analysis gives deeper insight compared to sentiment analysis. This research study aims to predict voting behavior for 2019 general election of India using affect analysis of multilingual tweets. Three languages namely English, Hindi and Gujarati are used for this study. Volume-based method and machine learning algorithm based method are two approaches widely used in literature for electoral prediction. In this research study hybrid approach is used along with consideration of ratio of positive count and negative count of tweets. Experiment result shows efficacy of the proposed approach.
A Cognitive Contemplation of Road Accident Predicton Through Deep Learning
Authors:   Research Scholar, Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies(VISTAS),Chennai, India., Priya* V. , Priya Dr. C. ,   Associate Professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies(VISTAS),Chennai, India.
Published: December 2019

The research based on the vehicle accidents step to collect and structure a progressive secure transportation unfortunately vehicle crashes were unavoidable. The accident prediction related with the risky environment data collection and arrangements based on the high priority of reality of accidents. The social activity and roadway structures are useful in the progression of traffic security control approach. We believe that to secure the best possible setback decline impacts with limited budgetary resources, it is basic that measures be established on coherent and objective studies of the explanations behind mishaps and seriousness of wounds. A survey based on the different algorithms able to predict the road accidents prevention methods. This paper demonstrates a couple of models to predict the reality of harm that occurred in the midst of car accidents using three artificial intelligent approaches (AI). The proposed scheme contributes a neural systems prepared utilizing choice trees and fluffy c implies bunching strategy for division.
Modeling Future Professional Activities of A Specialist in A Vocational Educational Institution
Authors:   Minin Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russian Federation, Igorevn* Vaganova Olga , Z.V. Smirnova ,   Minin Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russian Federation., I. N. Odarich ,   Federal State Budget Educational Institution of Higher Education «Togliatti State University», A.V. Lapshova ,   Minin Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russian Federation, M.M. Kutepov ,   Minin Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russian Federation, V. A. Razorenov ,   Minin Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russian Federation, E.A. Aleshugina ,   Nizegorodskij Gosudarstvennyj Arhitekturno-stroitel'nyj Universitet, Nizhny Novgorod, Russian
Published: December 2019

The process of future graduates’ professional activities development is a complex multi-stage process that requires continuous improvement. Preparation of students in construction majors requires special attention, since this area is constantly changing as technical progress is developing. The purpose of the article is to develop a model for future specialists’ professional activities development in the field of construction and operation of buildings and structures. This article analyzes the college curriculum for the major "Construction and maintenance of buildings and structures." Based on the data obtained, the objective necessity of improving the process of students’ future professional activity development was established. The authors developed a model for future specialists’ professional activities development in the field of construction and operation of buildings and structures, the target component of which is mastery of professional activities and development of professional competencies. This model includes necessary elements that are aimed at a more perfect development of students' professional activities. The model includes targeted, meaningful and effective components. The content component reveals the documents on which the process of modeling future specialists’ professional activities development should be based; methodological approaches ensuring implementation of modern educational paradigm; main functions of professional activity, its principles and stages of model implementation. Conclusion: the process of professional development of students requires constant improvement. The model developed by the authors is able to update it and improve the dynamics of professional competencies development.
Software Development Effort Duration and Cost Estimation using Linear Regression and K-Nearest Neighbors Machine Learning Algorithms
Authors:   Scholar, Sri JagadishPrasad JhabarmalTibrewala University, Rajasthan, India., Marapelli* Bhaskar
Published: December 2019

Effort estimation is a crucial step that leads to Duration estimation and cost estimation in software development. Estimations done in the initial stage of projects are based on requirements that may lead to success or failure of the project. Accurate estimations lead to success and inaccurate estimates lead to failure. There is no one particular method which cloud do accurate estimations. In this work, we propose Machine learning techniques linear regression and K-nearest Neighbors to predict Software Effort estimation using COCOMO81, COCOMONasa, and COCOMONasa2 datasets. The results obtained from these two methods have been compared. The 80% data in data sets used for training and remaining used as the test set. The correlation coefficient, Mean squared error (MSE) and Mean magnitude relative error (MMRE) are used as performance metrics. The experimental results show that these models forecast the software effort accurately.
Machine Learning Based Effective Classification of Distributed Denial of Service Attacks
Authors:   M.E., Department of Information Technology ,UIET, Panjab University, Chandigarh (Panjab), India., Bhardwaj* Aanshi , Mangat Veenu ,   M.E., Department of Information Technology ,UIET, Panjab University, Chandigarh (Panjab), India., Vig Renu ,   Ph.D, Department of Engineering and Technology, Panjab University, Chandigarh (Panjab), India.
Published: December 2019

Distributed Denial of Service Attack (DDoS) is a deadliest weapon which overwhelm the server or network by sending flood of packets towards it. The attack disrupts the services running on the target thereby blocking the legitimate traffic accessing its services. Various advanced machine learning techniques have been applied for detection of different types of DDoS attacks but still the attack remains a potential threat to the world. There are mainly two broad categories of machine learning techniques: supervised machine learning approach and unsupervised machine learning approach. Supervised machine learning approach requires labelled attack traffic datasets whereas unsupervised machine learning approach analyses incoming network traffic and then categorizes it. In this paper we have attempted to apply four different classifiers for the detection of DDoS attacks. The four classifiers applied are Logistic Regression, Naïve Bayes, K- Nearest Neighbor and Artificial Neural Network. The chosen classifiers provide stable results when there is a large dataset. We compared their detection accuracy on KDD dataset which is a benchmark dataset in the field of network security. This paper is novel as it explains each pre-processing step with python conversion functions and explained in detail all the classifiers and detection accuracy with their functions in python as well.
Public View on Capacity to Contract in Case of Occurrence of Fraud
Authors:   BA.,Llb (Hons) Saveetha School of Law, Saveetha Institute of Medical And Technical Science (Simats) , Saveetha University, Chennai, India., Thilagavathy* R. , Dhinesh M. ,   Assistant Professor, Saveetha School of Law, Saveetha Institute of Medical And Technical Science (Simats) Saveetha University, Chennai, India.
Published: December 2019

This Study is all about Capacity to Contract in Case Of Occurrence Of Fraud. The main aim of the study is to create awareness about Mer silence would amount to fraud. A man or woman is believed to have the potential to go into right into a contract. An intoxicated person, minor, or mentally incapable character has options to be had to them after getting into an agreement which influences the validity of the agreement into which they've entered. The first alternative they have got is to disaffirm a agreement. For the purpose of the study, descriptive research is used. Descriptive research helps to portray accurately the characteristics of particular individual, situation or group. Convenience sampling method is used in this study to collect the samples. When population elements are selected for inclusion in the sample based on access is called convenience sampling. The statistical tool used is percentage analysis, which includes Independent sample t test, Chi-square, Correlation. The total number of responses collected in the survey is around 1669 .It was found that constitution makes it mandatory for the Government to protect and promote freedom , and to assure every citizen a decent standard of living. In this study I concluded that Contract is nothing but enter into a formal and legally binding. It is a written or spoken obligation. And fraud, it's deliberate deception to secure unfair or unlawful gain.
Sign Language Recognition using Hybrid Neural Networks
Authors:   Ph.D, Scholar, IKG Punjab Technical University, Kapurthala, India., Singh* Shaminder , Gupta Anuj Kumar ,   Professor, Chandigarh Group of Colleges, Landran, India., Singh Tejwant ,   Dean (Retd.), College of Basic Sciences and Humanities PAU, Ludhiana, India.
Published: December 2019

Language has a prime role in communication between persons, in learning, in distribution of concepts and in preserving public contacts. The hearing-impaired have to challenge communication obstacles in a mostly hearing-capable culture. There are hundreds Sign Languages that are used all around the world today .The Sign Languages are established depending on the country and area of the deaf public. The aim of sign language recognition is to offer an effectual and correct tool to transcribe hand gesture into text. It can play a vital role in the communiqué between deaf and hearing society. Sign language recognition (SLR), as one of the significant research fields of human–computer interaction (HCI), has produced more and more interest in HCI society. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for sign language recognition in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system.
Towards Describing Visual Explanation using Machine Learning
Authors:   student of ME Computer Engineering at Sandip Institute of Technology and Research Centre (SITRC), Nashik, Maharashtra, India., Wadnere* Miss.D.G. , Thotam Mr. Naresh Chandramouli ,   Assistant Professor in Computer Engineering Department of Sandip Institute Of Technology & Research Centre, Nashik, Maharashtra, India.
Published: December 2019

Current available visible explanation generating systems research to easily absolve a class prediction. Still, they may additionally point out visible parameters attribute which replicate a strong category prior, though the proof may additionally not clearly be in the pic. This is specifically regarding as alternatively such marketers fail in constructing have confidence with human users. We proposed our own version, which makes a speciality of the special places of house of the seen item, together predicts the category label & interprets why the expected label is proper for the image. The machine proposes to annotate the images automatically using the Markov cache model. To annotate images, principles are represented as states through the usage of Hidden Markov model. The model parameters were estimated as part of a set of images and manual annotations. This is a great collection of checks, albeit automatically, with the possibility a posteriori of the concepts presented in her.