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Biochemical oxygen demand (BOD) is an important parameter for measuring the water quality especially the extent of water pollution due to organic compounds. The standard test for BOD requires a time period of 5 days with stringent conditions to be observed with regards to temperature, nutrients available and the lighting conditions suitable for the microbial growth. In order to predict BOD of river water in a cost-effective and efficient manner, in this paper a data driven ensemble method namely a Rotation Forest (RF) has been implemented. The model uses model trees M5 as base learners and hence the name rotation forest. Each base learner is trained using the rotated feature axes built on feature subsets computed using Principal Component Analysis (PCA). This helps to improve diversity in training the base learners and hence improves the predictive accuracy. Experimental analysis on available data sets shows that the correlation coefficient of a proposed approach is 0.9386 and RMSE of 0.5388. The predictive accuracy of this model is also compared with Multilayer Perceptron (MLP) neural networks model. However the proposed model has high correlation coefficient and low RMSE than MLP.

BOD, rotation forest, ensemble,M5,MLP, PCA,Correlation Coeffecient,RMSE

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J.A. Mangai, B.B. Gulyani, R. Khanam, "A Rotation Forest Algorithm for Predicting BOD in River Water", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.1-7, 2019.

The aim of this work is to load the goods into the container using the Garden Optimization Algorithm (GOA) to secure goods delivery. Using BR datasets, the effectiveness of the proposed approach was demonstrated and compared with various Bio inspired optimization algorithms. It is observed from the experiment that the proposed GOA is being implemented and fulfills the goal of optimally loading the goods into the container. It is also observed that the order in which the goods are placed in the container is optimal than other competitive optimization algorithms.

Container loading, Garden Optimization algorithm, secure delivery, Optimization algorithm

[1] Xueping Li, Kaike Zhang 3015, ‘A hybrid differential evolution algorithm for multiple container loading problem with heterogeneous containers’, Computers & Industrial Engineering, vol. 90, no. 7, pp. 105-111.
[2] Tansel Dokeroglu, Ahmet Cosar 3014, ‘Optimization of one dimensional Bin Packing Problem with island parallel grouping genetic algorithms’, Computers & Industrial Engineering, vol. 75, no. 1, pp. 176-186.
[3] Kyungdaw Kang, Ilkyeong Moon, HongfengWang 3013, ‘A hybrid genetic algorithm with a new packing strategy for the three dimensional bin packing problem’, Applied Mathematics and Computation, vol. 319, no. 1, pp. 1387-1399.
[4] D.S.Liu K.C.Tan, S.Y.Huang, C.K.Goh, W.K.Ho 3008, ‘On solving multiobjective bin packing problems using evolutionary particle swarm optimization’, European Journal of Operational Research, vol. 190, no. 3, pp. 157-183.
[5] Teodor Gabriel Crainic, Luca Gobbato, Guido Perboli, Walter Rei 3016, ‘Logistics capacity planning: A stochastic bin packing formulation and a progressive hedging metaheuristic’, European Journal of Operational Research, vol. 351, no. 3, pp. 404-417.
[6] Alessio Trivella, David Pisinger 3016, ‘The load balanced multidimensional bin packing problem’, Computers & Operations Research, vol. 74, no. 4, pp. 153-164.
[7] Antonio G.Ramos, Elsa Silva, Jose F. Oliveira 3018, ‘A new load balance methodology for container loading problem in road transportation’, European Journal of Operational Research, vol. 366, no. 1, pp. 1140-1153.
[8] Galrao Ramos, Jose F. Oliveira, Jose F. Goncalves, Manuel P. Lopes 3016, ‘A container loading algorithm with static mechanical equilibrium stability constraints’, Transportation Research Part B: Methodological, vol. 91, no. 1, pp. 565-581.
[9] I. Araya, M. C. Riff 3014, ‘A beam search approach to the container loading problem’, Computers & Operations Research, vol. 41, no. 1, pp. 100-107.
[10] Turkay Derelia, Gulesin Sena Das 3011, ‘A hybrid ‘bee(s) algorithm’ for solving container loading problems’, Applied Soft Computing, vol. 11, no. 3, pp. 3854-3863.
[11] container loading problem’, Computers & Operations Research, vol. 19, no. 3, pp. 179-190.

R. Sathish Kumar, "Effective Loading of Goods into the Container using Garden Optimization Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.8-11, 2019.

If we live in a metropolitan city, traffic is an immense problem of our day to day life. The fundamental explanations for this is, increase of individual vehicles, poor street infrastructure, absence of appropriate road and old ordinary methods for managing traffic. We need to spend a lot of time in rush hour gridlock. Also this leads to a lot of fuel combustion which is a major cause of pollution and health hazards. We have seen that even though amount of vehicles along a particular traffic signal is less, it runs according to a particular allotted time. By using this algorithm we will predict the exact optimal time required by a traffic signal to be made green based on the amount of vehicles present in its lane. In this paper we are trying to minimize traffic clog issue with the assistance of distributive traffic control, using object identification strategy (YOLO) for vehicle counting. The productivity of our proposed framework lies in the fact that this system (DTCS) manages traffic signals depending on the present circumstance of vehicular volume present in its lane and not on pre-assigned time. We have compared two different techniques for counting vehicles which is edge detection and the current state of the art YOLO algorithm.

Traffice Congestion, Distributed traffice control, edge detection, You only Look Once (Yolo)

[1] Amrita Rai and Govind Singh Patel Multiple Traffic Control Using Wireless Sensor and Density Measuring Camera. Sensors & Transducers Journal Vol. 94, Issue 7, July 2008, pp. 126-132.
[2] W. Wen & C.L. Yang. “A dynamic and automatic traffic light control system for solving the road congestion problem”.
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[4] Bin-Feng Lin; Yi-Ming Chan; Li-Chen Fu; Pei-Yung Hsiao; Li-An Chuang; Shin-Shinh Huang; Min-Fang Lo; "Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-Spot Area," Intelligent Transportation Systems, IEEE Transactions on , vol.13, no.2, pp.737,747, June 2012.
[5] P. Rajesh, M. Kalaiselvi Geetha and R.Ramu; “Traffic density estimation, vehicle classification and stopped vehicle detection for traffic surveillance system using predefined traffic videos”,Elixir Comp. Sci. & Engg. 56A (2013) 13671-13676.
[6] Deng-Yuan Huang, Chao-Ho Chen, Wu-Chih Hu, Shu-Chung Yi, and Yu-Feng Lin; “Feature-Based Vehicle Flow Analysis and Measurement for a Real-Time Traffic Surveillance System”, Journal of Information Hiding and Multimedia Signal Processing, Volume 3, Number 3, pp. 2073-4212, July 2012.
[7] Hashmi, M.F.; , A.G., "Analysis and monitoring of a high density traffic flow at T-intersection using statistical computer vision based approach," Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on , vol., no., pp.52,57, 27-29 Nov. 2012.
[8] Suvarna Nandyal1, Pushpalata Patil; “Vehicle Detection and Traffic Assessment Using Images”, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 2, Issue. 9, September 2013, pg.8 – 17.
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[10] Feris, R.S.; Siddiquie, B.; Petterson, J.; Yun Zhai; Datta, A.; Brown, L.M.; Pankanti, S., "Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos," Multimedia, IEEE Transactions on , vol.14, no.1, pp.28,42, Feb. 2012.
[11] Chieh-Ling Huang; Heng-Ning Ma, "A Moving Object Detection Algorithm for Vehicle Localization," Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on , vol., no., pp.376,379, 25-28 Aug. 2012.
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[13] Prashanth kumar, b. Karthik “micro controller based traffic light controller”, Department of Electrical & Electronics Engineering gokaraju rangaraju institute of engineering & technology, 2011.
[14] Sachin Jaiswal*, Tushar Agarwal*, Akanksha Singh*and Lakshita* ” Intelligent Traffic Control Unit”, *Department of Electronics and Communication Engineering, Bharati Vidyapeeth‟s College of Engineering, Paschim Vihar, New Delhi-110063.
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[20] Naik, T., Roopalakshmi, R., Ravi, N. D., Jain, P., & Sowmya, B. H. (2018, April). RFID-Based Smart Traffic Control Framework for Emergency Vehicles. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 398- 401). IEEE

Shaleen Bhatnagar, Yugansh Bhatnagar, "Distributed Trafic Control System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.12-17, 2019.

Cloud computing is a fast-growing technology that is being used extensively in the market today. Most cloud service providers, provide clients with one of two things either resources or services as entitled to the client by the SLA. In resource allocation a user may want to gain access to multiple resources at the same time, thus we require a method for the optimal provision of resources. With the sudden massive growth in cloud computing there is no dearth of techniques and algorithms that can be used for resource allocation. In this paper we will study the various resource scheduling algorithms being utilized in the virtualization industry and compare them on common factors to discern the comparatively optimal resource scheduling algorithm.

Resources,services,optimal,virtualization,resource allocation

[1] J. K. Author, “Title of dissertation,” Ph.D. dissertation, Abbrev. Dept.,
[2] Peter Sempolinski, Douglas Thain, “A Comparison And Critique Of Eucalyptus, Open Nebula And Nimbus”, Cloud Computing Technology And Science (CloudCom),DOI 10.1109/CloudCom.2010,Page(s):417-426
[3] Ms.NITIKA, “Comparative Analysis Of Load Balancing Algorithms In Cloud Computing ”, International Journal Of Engineering And Science, 2011,ISSN:2319- 1813,Page(s): 34-38
[4] Shridhar G.Domanal and G.Ram Mohana Reddy “Load Balancing In Cloud Computing Using Modified Throttled Algorithm”, IEEE International Conference on Cloud Computing in Emerging Markets, Oct, 2013,Page(s): 1-5
[5] Hamid Shoja ,Hossein Nahid, Reza Azizi “ A Comparative Survey On Load Balancing Algorithms In Cloud Computing”, International Conference On Computing, Communication And Networking Technologies, July,2014 , Page(s):1-5
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[7] George Chang, Shan Malhotra, Paul Wolgast, “Leveraging The Cloud For Robust And Efficient Lunar Image Processing”,IEEE, 2011, Page(s):1-8

Divya Tantri, Karan George, S. Rajarajeswari, "A Study of Virtual Machine Resource Scheduling Algorithms in Cloud Computing Environment", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.18-33, 2019.

In this paper, we propose a hybrid rule-based approach, named as Optimal Email Feature Selection (OEFS) Protocol for selecting optimal features to reduce the searching time in detecting spam emails. The OEFS protocol performs email spam detection in four stages: Feature Selection, Normalization of selected features, Rank Assignment and Optimal Feature Selection. The OEFS protocol has been executed and designed for large data amount of data by achieving accurate feature generation. The performance of OEFS analyzed using different protocols in existing systems. The protocol defines here an optimality for email spam detection and correction which provides an optimal solution and outperforms all email filtering protocols like PEP and CRVSM.

Optimal Feature, Normalization, Score Assignment, Spam Email

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[3] Jingle, D.J. and Rajsingh, E.B.,“ColShield: an effective and collaborative protection shield for the detection and prevention of collaborative flooding of DDoS attacks in wireless mesh networks,” Human-centric Comp. and Inf. Sci., Springer Publications, Vol. 4, Issue 8, 2014
[4] Jingle, D.J., Rajsingh,E.B. and Paul, M., “Distributed Detection of DoS Using Clock Values in Wireless Broadband Networks,” Int. J. of Engg. and Advanced Tech. (IJEAT), Vol. 1, Issue 5, June 2012.
[5] Jingle, D.J and Rajsingh, E.B., “DDDOST: Distributed detection of DOS attack using timers in wireless broadband networks,” Int. Conf. on Advanced Comp. (ICoAC), IEEE, ISSN : 2377-6927, DOI : 10.1109/ICoAC.2012.6416795, 2012.
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[7] Paul, M. and Ravi R., “A Collaborative Reputation-based Vector Space Model for Email Spam Filtering”, J. of Comput. and Theoret. Nanosci., Vol. 15, No.2, Pages 474-479, February 2018, doi.org/10.1166/jctn.2018.7128, American Scientific Publishers, 2018
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[11] Vitor Basto-Fernandes, Iryna Yevseyeva, José R. Méndez, Jiaqi Zhaod, Florentino Fdez-Riverola, Michael T.M. Emmerich, “A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification”, Appl. Soft Comp., Vol. 48, Pages 111-123, 2016
[12] Wazir Zada Khan, Muhammad Khurram Khan, Fahad Bin Muhaya, Muhammad Y Aalsalem and Han-Chieh Chao, “A Comprehensive Study of Email Spam Botnet Detection”, IEEE Comm. Surv. & Tut..
[13] Youwei Wang, Yuanning Liu, Lizhou Feng, Xiaodong Zhu, “Novel feature selection method based on harmony search for email Classification”, Knowl. - Based Syst., Vol. 73, Pages 311 - 323, http://dx.doi.org/10.1016/j.knosys.2014.10.013, Elsevier B.V., January 2015

P.Mano Paul, I. Diana Jeba Jingle, "A Novel optimal Email Feature Selection Protocol (OEFS) for Detecting Spam Emails", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.34-39, 2019.

Image compression take a important part in digital world. Storing and transmitting digital image with high quality is a complex task. There are many methods for compressing digital images. In this paper, the following method is adapted. The digital image is divided into low and high intensity images. Discrete Cosine Transform (DCT) technique is applied to high intensity part of the image and fast Fourier transform (FFT) method is applied for low intensity pixels. The proposed method is tested with benchmark images and the results are compared with JPEG 2000 (Joint Photographic Experts Group 2000). It provides better results than JPEG 2000.

JPEG 2000, Lossless, Lossy compression, Discrete Cosine Transform, Fast Fourier transform, VBS

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V. Lakshmi Praba, R. S. Rajesh, S.Anitha, "Hybrid coding for image compression", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.40-42, 2019.

Agriculture is one of the main central divisions of Indian Providence. Indian agriculture segments more than 18 per cent of India’s household product further it helps to provide employment opportunities to 50% of the countries workforce. Fungus, viruses and germs are the main causes that affect the plant leaves. Currently crops face many types of diseases. Damage by the pest is main trait. If good concern is not done in the plants it may lead to grave outcome on vegetation. Discovery of plant illness along with the other routine method in monitoring the plants reduces the drudgery as it ascertains the injection of plants at early stage. Image processing provides better techniques about disease identification. The process has four major processes, initially the color image is transformed to RGB, and then the RGB image is then transferred to HSV for shade generation. After that the green pixels are obtained from the color generation process. The image is analyzed, segmented meaningfully and the texture features are extracted and evaluated from the SGDM matrices.

HSI, RGB, Texture, SGDM, Texture, Image

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[2] Prof. Sanjay B. Dhaygude, Mr.Nitin P.Kumbhar “Agricultural plant Leaf Disease Detection Using Image Processing” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 1, January 2013.
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[5] Sabah Bashir, Navdeep Sharma “Remote Area Plant Disease Detection Using Image Processing”, IOSR Journal of Electronics and Communication Engineering , ISSN : 2278-2834 Volume 2, Issue 6 2012, PP 31-34.
[6] Smita Naikwadi, Niket Amoda” ADVANCES IN IMAGE PROCESSING FOR DETECTION OF PLANT DISEASES”, International Journal of Application or Innovation in Engineering & Management , Volume 2, Issue 11, November 2013.
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[8] Muhammad Hameed Siddiqi1, Suziah Sulaiman, Ibrahima Faye and Irshad Ahmad, A Real Time Specific & Weed Discrimination System Using Multi-Level Wavelet Decomposition, International Journal of Agriculture Biology, ISSN Print: 1560–8530; ISSN Online1814-9596 ,09– 118/YHP/2009/11–5–559–565.
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S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi, "Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.43-46, 2019.

In medical specialty the human eye is a very important diagnostic issue. The problem of retinal detachment is commonly for the people over the age of 50 and it affects the people who had previous eye surgery like cataract removal and also severe eye injuries. The Segmentation in fundus imaging that is a non-trivial task because of the variable size of vessels, comparatively low distinction, and potential presence of pathologies like micro-aneurysm. Many machine learning, deep learning algorithms, have been proposed for this purpose. This paper provides recently invented ideas to improve the technique for blood vessel segmentation to enhance retinal fundus photographic images. Many variants of segmentation methods are considered, including Tyler L. Coye where is an improved version of segmentation methodology used to segment the blood vessels for fundus photography image. The proposed approach was tested and evaluated on Agarwal Eye Hospital’s fundus dataset which consists of 100 photographic images.

Pre-processing, Image Enhancement, Tyler Coye Segmentation Algorithm, Image extraction Retinal Detachment (RD)

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K. Deviga, S. Ramalakshmi, T. Ratha Jeyalakshmi, "Contribution of Machine Learning and Deep Learning for Diagnosis of Retinal Detachment", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.47-50, 2019.

Collected data directly from examples, data, and past experience is called Machine Learning. Cancer diagnosing could be an extremely difficult field in Machine Learning. Skin cancer is nothing but it be a dangerous disease associated it’s found as an uncontrolled growth of abnormal skin cells. Image enhancement method is utilized to remove unwanted scales (median filtering and salt and pepper technique) in image. Then the projected methodology helps in the section of cancer footage. Finally, Principal component Analysis is employed to concentrates on the melanoma’s exists and Grab cut methodology is utilized for the feature extraction of melanoma mole from skin.

Machine Learning, Melanoma, grab- Cut, Median filtering, Segmentation, Preprocessing

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R. Veeralakshmi, T. Ratha Jeyalakshmi, "Grab-cut Algorithm in Machine Learning for Diagnosing Melanoma", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.51-54, 2019.

In recent years, video police work systems are typically adopted more or less the planet as security issues and their low hardware price. Anomaly detection is one in all the analysis areas within the field of video police work. During this study, totally different existing cluster primarily based, like techniques EM bunch and classification primarily based anomaly detection techniques in video police work square measure mentioned. The video closed-circuit television includes background modeling, object detection, object following, activity recognition and classification. Recently, the machine learning primarily based anomaly detection techniques plays a significant role within the classification of the events into traditional and abnormal events. The new approaches just like the grouping of Convolution Neural Network and repeated Neural Network and cascade deep learning square measure the strong algorithms for big datasets. The options so extracted square measure fed to a Discriminative Deep Belief Network. Labeled videos of some uncertain activities also are fed to the DDBN and their options also are extracted. Then the options extracted exploitation Convolution Neural Network square measure compared against these options extracted from the labeled sample video of classified suspicious actions employing a Discriminative Deep Belief Network (DDBN) and varied suspicious actions square measure detected from the given video.

Convolution Neural Network, Discriminative Deep Belief Neural Network, Recurrent neural network, video surveillance

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M.Petchiammal@Baby, S. Santhiya, T. RathaJeyalakshmi, "Unusual Activity Detection in Surveillance Video using Machine Learning and Discriminative Deep Belief Network Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.55-59, 2019.