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Distributed routing algorithms can lead to a temporary elevation between path regeneration, which can cause major stability problems in high-speed networks. This paper introduces a new algorithm, Distributed Path Computing with Intermediate Variables (DPCIV), which can be integrated with any distributed algorithm to ensure that the directed graph caused by route decisions is always acyclic. An important contribution of DPCIV, in addition to its ability to work with any routing algorithm, is the update method using simple message exchanges between neighboring locations that ensure maximum ease at all times. DPCIV apparently outperforms existing loop blocking algorithms in key metrics such as the frequency of synchronized refresh and the ability to save paths during the transition. The simulation results that block these advantages in the context of a very short path are presented. In addition, the universal performance of DPCIV is demonstrated by studying its use of a functionally-oriented non-shortcut. In particular, the route seeks to counteract the power of failure by increasing the number of subsequent hops available at each destination.

Distance vector, shortest path, link state, routing algorithm

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[14] S. Dubey1 , R. Jhaggar, R. Verm , D. Gaur, “Encryption and Decryption of Data by Genetic Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.42-46, June 2017.
[15] Amr M. Kishk1, Nagy W. Messiha, Nawal A. El-Fishawy, Abdelrahman A. Alkafs and Ahmed H. Madian, “Proposed Jamming Removal Technique for Wireless Sensor Network”, International Journal of Scientific Research in Network Security and Communication, Vol.3, Issue. 2, April 2015.

K.V. Nishad, R. Vadivel, "Distributed Path Computation with Intermediate Variables (DPCIV) for Distributed Routing Algorithms to Guarantee Routing Decisions," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.59-63, 2020.

One of the major pollutants of environment is waste, if it is not managed and controlled, it will cause health hazards and increase environmental pollution. Nowadays we observe much improvement in technology, most of the developed countries manage the waste using smart systems and automated machines. Some countries still use the traditional waste management system. Due to rapidly increasing of population, huge amount of waste is generated daily. Traditional waste management system has some drawbacks and problems, for instance, producer maintains the waste as mixed and collector also collects as mixed. There is no monitoring system on producer and collector, some producers throw the garbage around the trash bins, some collectors even do not collect the waste, so neither producer and nor collector do their job properly. To overcome the problems and manage wastes efficiently, this paper has proposed online (Geographic Information System) GIS based mobile application system. The producers and collectors of waste are monitored through online platform and requests the producers to maintain biodegradable and non-biodegradable wastes separately. Using mobile application, producer requests the system for collection of waste, meanwhile the system assigns the task to collector which is near to producer’s location. The collector reaches to the location through the best path and collects the wastes as separate. Additional features and functionalities of the system also discussed in this paper.

Smart Waste Management System, Geographic Information System, Biodegradable and Non-Biodegradable waste, Mobile Application

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[5] D. S. Anand, P. R and A. S. I, "Smart Waste Management System," International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 3, no. III, pp. 515-518, 2017.
[6] S. Labib, "Volunteer GIS (VGIS) Based Waste Management," IEEE, pp. 137-141, 2017.
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[10] A. S. Wijaya, Z. Zainuddin and M. Niswar, "Design a Smart Waste Bin for Smart Waste Management," IEEE, pp. 62-66, 2017.
[11] Atada, A. S, Sankhya and Sharma, "Creating smartness in people towards waste management," IEEE, 2017.
[12] e. a. Kumar, "Eco-Friendly IOT Based Waste Segregation and Management," IEEE, pp. 297-299, 2017.
[13] Arebey, Hannan, Basri and Abdullah, "Solid Waste Monitoring and Management using RFID, GIS and GSM," IEEE, pp. 37-40, 2009.
[14] Zeeshan, Shahid, Khan and Shaikh, "Solid Waste Management in Korangi District of Karachi using GPS And GIS: A Case study," IEEE, pp. 384-387, 2018.
[15] S. Sahana and S. Basak, "GIS BASED APP ENABLED MONITORING SYSTEM FOR CLEAN SMART CITY," International Journal of Latest Trends in Engineering and Technology, vol. 9, no. 4, pp. 109-114.
[16] C. Rao, P.Pushpalatha and N. Sundar, "GPS Based Vehicle Navigation System Using Google Maps," International Journal of Computer Science and Information Technologies (IJCSIT), vol. 4 (6), pp. 979-982, 2013.

Sayed Abdul Saboor Osmani, Ambrish G., "Smart Waste Management: A Conceptual Design and Analysis of GIS Based Real Time Waste Management using Mobile Application," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.64-69, 2020.

In the world of stock market Machine Learning has a very unique role to play when it comes on to the stock prediction. Machine learning library which is also known as MLIB helps in determining the future values of the stocks. This Research finds out the future ups and downs of stock market by providing you a signal for the same, whether the stock will be closed up or down. This has done by analysing the historical data. In this study stock data of NSE (National Stock Exchange of India) from 2000 to 2019 have been analysed which includes top forty eight companies of various sectors from all over India. With the help of machine learning libraries six technical indicators known as Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams %R, Moving Average Convergence Divergence (MACD), Rate of Change have been applied on to the nineteen years of stock data and finally, Random Forest algorithm and Artificial Neural Network Model have been applied on it to predict the stock movement, at last a comparison between Random forest and ANN model has also been done to check the better prediction.

Stock data, Nifty-50, Stock Indicators, Random Forest, Artificial Neural Network

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[2] P. Aithal, A. Dinesh and M. Geetha, "Identifying Significant Macroeconomic Indicators for Indian Stock Markets", IEEE Access, vol. 7, pp. 143829-143840, 2019.
[3] Y. Alsubaie, K. Hindi and H. Alsalman, "Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators", IEEE Access, vol. 7, pp. 146876-146892, 2019.
[4] A. Giri and P. Joshi, "The Impact of Macroeconomic Indicators on Indian Stock Prices: An Empirical Analysis", Studies in Business and Economics, vol. 12, no. 1, pp. 61-78, 2017.
[5] P. Kanade, "Machine Learning Model for Stock Market Predi- ction", International Journal for Research in Applied Science and Engineering Technology, vol. 8, no. 6, pp. 209-216, 2020.
[6] H. M, G. E.A., V. Menon and S. K.P., "NSE Stock Market Prediction Using Deep-Learning Models", Procedia Computer Science, vol. 132, pp. 1351-1362, 2018.
[7] M. Vijh, D. Chandola, V. Tikkiwal and A. Kumar, "Stock Closing Price Prediction using Machine Learning Techniques", Procedia Computer Science, vol. 167, pp. 599-606, 2020.
[8] Sharma, N., & Juneja, A., “Combining of random forest estimates using LSboost for stock market index prediction”. 2017 2Nd International Conference For Convergence In Technology (I2CT).
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[15] M. Firdaus, S. Pratiwi, D. Kowanda and A. Kowanda, "Literature review on Artificial Neural Networks Techniques Application for Stock Market Prediction and as Decision Support Tools", 2018 Third International Conference on Informatics and Computing (ICIC).
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Ankit Kumar, Jasbir Singh Saini, "Stock Data Analysis and Prediction in Machine Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.70-78, 2020.

The Object Oriented Programming paradigm has revolutionized the process of software development. It provides a great control over data and offers various revolutionary features like abstraction, encapsulation, polymorphism, inheritance that facilitates reusability of previous efforts done to build softwares. This approach makes it possible to develop softwares as reusable component that can be assembled with other. This software development paradigm makes it possible to develop software applications based on „plug and play? in which we can add, replace or modify components according to our needs. This Component Based Software development approach provides a cost effective, fast and modular approach for developing complex software with reduced delivery time. The technologies that facilitate Component development are distributed object technologies. The Distributed Object technology allows objects active in one process be accessed by another facilitating the computation be split over multiple processes. The processes involved may be running in different address spaces on single system or may be on different systems in a network in a local area network or the Internet. The most popular distributed object technologies are CORBA, RMI and DCOM. This paper presents an analysis of architecture and working of these technologies and compares software development methodology in these technologies on the basis of key terminologies used such as data marshalling, interoperability, heterogeneity, design transparency and speed.

Software Components, Components based Software Development, Distributed Objects, CORBA, RMI, Marshalling, Interoperability, Heterogeneity, Design Transparency

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[17] Ivica Crnkovic and Magnus Larsson,”Challenges of component-based development”, published in the Journal of Systems and Software,(2002) 201–212.
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Sanjay E. Yedey, "Component Based Software Development using Distributed Objects," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.79-84, 2020.

Cloud computing (CC) provides an economy of scale, efficient resources usage, and the availability of the resources to a large user base. Despite the provision of electronic infrastructure by Malaysian government and spending billions of dollars annually for cloud computing adoption, its level among SMEs is still low. Therefore, the key objective of the current research is to propose a cloud computing adoption model to determine technology attributes that affect the adoption in Malaysian SMEs, as technology component suffers the most significant threat against cloud adoption. The research model is based on Technology Organization Environment Framework (TOE) and its extended conceptual model by Opala. The technology factors include service level, security, cost effectiveness, IT compliance, reliability, flexibility and confidentiality. The primary data collection was conducted using the survey method. A total of 225 questionnaires were collected from IT Executives of the SMEs in Malaysia. Quantitative data was analyzed statistically using SPSS and AMOS software version 21. The results of the data analysis show that the exogenous variables, reliability, confidentiality, flexibility, service level explained 69% of the variance in security effectiveness and explained 41% of the variance in cost effectiveness as well as explained 33% of the variance in IT compliance.

Cloud Computing Adoption, Technology Factors, SMEs

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Abdulhafid Bughari Abdulkarim Abdulgadr, Salem El-Bahlool Elbakai, "Technological Factors of Cloud Computing Adoption Model for Malaysian Small and Medium Enterprises (SMEs)," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.85-90, 2020.

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However, the need to develop new model that considers both weather factors and non-weather data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier-based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere, Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperature Difference, Relative Humidity, Stages of Paddy Cultivation, Varieties of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. In the collected data some of the variables are non-numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers 4 different filter-based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Further three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Along with this other classification metrics are used evaluate Ensemble classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifiers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is good compared to other ensemble classifiers that are proposed in predicting paddy blast disease.

Paddy Blast Disease, Mutual Information, ANNOVA F Value, Voting Classifer, Bagging, Adaboost, Precision-recallScore,ROC

[1] Shafaullah, Muhammad Aslam Khan, Nasir Ahmed Khan And Yasir Mahmood, “Effect Of Epidemiological Factors On The Incidence Of Paddy Blast (Pyricularia Oryzae) Disease,” Pak J.Phytopayhol.,Vol 23(2):108-111, 2011.
[2] Kwang-Hyung Kima, Jaepil Cho, Yong Hwan Lee, Woo Seop Lee, “Predicting potential epidemics of rice leaf blast and sheath blight in South Korea under the RCP 4.5 and RCP 8.5 climate change scenarios using a rice disease epidemiology model, EPIRICE”, Elsevier Agricultural and Forest Meteorology 203(2015) 191 207.
[3] Yoshihiro Taguchi, Mohsen Mohamed Elsharkawy, Naglaa Hassan, Mitsuro Hyakumachi, “ A novel method for controlling rice blast disease using fan-forced wind on paddy fields”, Elsevier, Crop Protection 63(2014) 68-75.
[4] Kwang-Hyung Kim & Jaepil Cho, “Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information”, Springer Climatic Change (2016) 134:327–339 DOI 10.1007/s10584-015-1503-2.
[5] G. Miah, M. Y. Rafii, M. R. Ismail, M. Sahebi, F. S. G. Hashemi,O. Yusuff and M. G. Usman, “Blast Disease Intimidation Towards Rice Cultivation: A Review Of Pathogen And Strategies To Control”, The Journal of Animal & Plant Sciences, 27(4): 2017, Page: 1058-1066 ISSN: 1018-7081.
[6] Rupankar Bhagawati, Kaushik Bhagawati†, D. Jini, R. A. Alone, R. Singh, A. Chandra, B. Makdoh, Amit Sen and Kshitiz K. Shukla, “Review on Climate Change and its Impact on Agriculture of Arunachal Pradesh in the Northeastern Himalayan Region of India”, Nature Environment and Pollution Technology An International Quarterly Scientific Journal p-ISSN: 0972-6268 e-ISSN: 2395-3454 Vol. 16 No. 2 pp. 535-539 2017.
[7] Rajendra Prasad, Anupam Sharma and Sweta Sehgal, “ Influence of weather parameters on occurrence of rice blast in mid hills of Himachal Pradesh”, Himachal Journal of Agricultural Research 41(2): 132-136, 2015.
[8] Rakesh Kaundal, Amar S Kapoor and Gajendra PS Raghava, “Machine learning techniques in disease forecasting: a case study on rice blast prediction”, BMC Bioinformatics Journal 7:485 doi:10.1186/1471-2105-7-485, 2006.
[9] Y.H. Gu, S.J. Yoo, C.J. Park, Y.H. Kim, S.K. Park, J.S. Kim, J.H. Lim, “BLITE-SVR: New forecasting model for late blight on potato using support-vector regression”, Elsevier, Computers and Electronics in Agriculture 130(2016) 169-176.
[10] Jia-You Hsieh, Wei Huang, Hsin-Tieh Yang, Chia-Chieh Lin, Yo-Chung Fan, Huan Chen, “Building the Rice Blast Disease Prediction Model based on Machine Learning and Neural Networks”, EasyChair Preprint.
[11] Alvin R. Malicdem, Proceso L. Fernandez, “Rice Blast Disease Forecasting for Northern Philippines”, Article in WSEAS Transactions on Information Science and Applications • January 2015.
[12] Yangseon Kim, Jae-Hwan Roh and Ha Young Kim, “ Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks”, Journal on Sustainability, 10, 34; doi:10.3390/su10010034 2018.
[13] Dimitrios Katsantonis1 , Kalliopi Kadoglidou, Christos Dramalis And Pau Puigdollers, “Rice blast forecasting models and their practical value: a review”, Journal on Phytopathology Mediterranean, 56, 2, 187?216 DOI: 10.14601/Phytopathol_Mediterr-18706 2017.
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[15] P. B. Jawade, Dattatray Chaugule, Devashri Patil, and Hemendra Shinde, “ Disease Prediction of Mango Crop Using Machine Learning and IoT”, International Joint Conference on e-Business and Telecommunication( ICETE 2019) , LAIS 3, pp. 254–260, 2020.
[16] Karim Foughali, Karim Fathallah, Ali Frihida, “Using Cloud IOT for disease prevention in precision agriculture”, International Conference on Sustainable Energy Information Technology, SEIT 2018, Porto, Portugal May 2018.
[17] Sehan Kim, Meonghun Lee, Changsun Shin, “IoT-Based Strawberry Disease Prediction System for Smart Farming” , Sensors , 18, 4051; doi:10.3390/s18114051 2018.
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[20] Shadi Aljawarneh, Muneer Bani Yassein, Monther Aldwair, “ Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model”, Journal of Computational Science 152-160, 28, 2018.
[21] S.I. Mostafa , Y.M. Abd El-Latif , N.M. Reda, “ Fast And Accurate System For Leaf Recognition”, International Journal of Computer Sciences and Engineering Vol.8, Issue.8, August 2020.
[22] R. Thirumahal , G. Sudha Sadasivam, “Data Integration Techniques For Healthcare – A Comprehensive Survey”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, August 2020.

Varsha M., Poornima B., "A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.91-100, 2020.

Voice activity detection is classifying speech and non-speech frames. Effectively working and noise tolerant voice activity detection technique is responsible for better performance of many new speech technologies in the area of speech processing. In this paper, an unsupervised method for VAD is proposed to identify the segments of speech- presence and speech-absence in an audio. To make the presented algorithm effective and computationally fast, it is implemented by using long-term parameters that are extracted by using Petrosian algorithm used for fractal dimensions. This system plays a significant role in terms of achieving improved speech quality. Two types of datasets recorded in English and Arabic languages are used to analyses the output of the proposed algorithm. An Array of 85 audio signals of TIMIT Database, of different Signal to noise ratios is tested using the algorithm at once. The evaluated performance suggested that the proposed algorithm identifies segments in the audios with different SNR’s

Fractal Dimensions

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S. Kaur, P. Mittal, "Classification of Audio Segments using Voice Activity Detection," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.101-105, 2020.

Gardening is a common pastime for people who love nature. Plants always requires a careful and continues monitoring. Most of the time it will turn into a responsibility. Sometimes garden owner need to go out for a while, then garden may keep on unattended. Internet of thing can give a possible solution for this problem. Environmental condition of the garden can be monitored continuously by using an electronic technology in the garden. Using IoT (Internet of Things), environmental condition in the garden can be monitor through the internet. Gardening is a very exciting to do. So I was very interested in making a project related to it. Nowadays people will enjoy gardening by using an IoT application to it (smart Garden). Moreover the important task here is making the project very simple so that anybody can use this in their garden. In this proposed project garden monitoring system is developed. Here the multiple nodes are designed to collect data from garden and sent to the main node that will upload the data to server where people can assimilate all the condition of their garden.

Raspberry Pi, Arduino board, Light sensor, Soil moisture sensor, Humidity and Temperature sensor

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Sindhu P., Leena Giri G., "Real Time IoT Application of Urban Garden Design," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.106-109, 2020.