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• Open Access   Article

  A Recommendation Engine to Estimate Housing Values in Real Estate Property Market

  Stanley Ziweritin, Chibuzo Chimezie Ukegbu, Taiwo Adisa Oyeniran, Ikpo Ochu Ulu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.1-7, Feb-2021

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  Abstract
  A recommendation engine is an automated machine learning technique developed to filter some entities called people, properties or objects, products and movies based on user preference. This can be employed to express recommendation based on user interest determined by its market value. The increasing demand and limited in supply of housing properties limits the existing methods of operation to bear a heavy burden in recommending housing properties. This leads to information overload which made it difficult to search and recommend properties with respect to user`s specific interest. These can be affected by its location and condition of property. Therefore; we proposed the use of a recommendation engine with collaborative technique in providing optimal solution to the challenges facing the housing market. This work is aimed at developing an efficient recommendation engine to estimate housing values. This serves to overcome the problem of information overload, help understand what the user wants and recommend properties with the most popular features. The implementation was done using logistic regression and K-nearest neighbor techniques with Python. The performance was improved with some fine-turned hyper-parameter values using the kaggle online dataset. The K-nearest neighbor produced 100% prediction accuracy recorded to be better than the logistic regression with 54.6% accuracy rate.

  Key-Words / Index Term
  Collaborative Filtering, KNN, Logistic regression, Recommendation Engine

  References
  
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  Citation
  Stanley Ziweritin, Chibuzo Chimezie Ukegbu, Taiwo Adisa Oyeniran, Ikpo Ochu Ulu, "A Recommendation Engine to Estimate Housing Values in Real Estate Property Market," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.1-7, 2021

• Open Access   Article

  Deep Learning Approach Using Long Short Term Memory Technique for Monthly Rainfall Prediction in Chhattisgarh, India

  Nisha Thakur, Sanjeev Karmakar

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.8-13, Feb-2021

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  Abstract
  Rainfall is an essential factor in Chhattisgarh state as the economy is dependent on agriculture here. Time Series forecasting approach for monthly rainfall prediction is done using Long Short Term Memory [LSTM] Model applying on 1404 months data of Chhattisgarh state. The factors taken for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Cosine Similarity (CS) and Correlation Coefficient (r). Various learning rate like (? = 0.01, 0.05, 0.001, 0.005) for various epoch(s) such as 200, 400, 600, 800 and 1000 respectively are done for LSTM approach. The experimental results show that Long Short Term Memory gave significant results than ANN for 200 epochs.

  Key-Words / Index Term
  Forecasting; Long short-term memory; Recurrent neural networks; Time series; Artificial Neural Network (ANN)

  References
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  Citation
  Nisha Thakur, Sanjeev Karmakar, "Deep Learning Approach Using Long Short Term Memory Technique for Monthly Rainfall Prediction in Chhattisgarh, India," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.8-13, 2021

• Open Access   Article

  Privacy Enhancement in Biometric Systems by Template Protection using Deep Learning

  Harshita Kukreja, Soumya Jain, Shefali Arora, M.P.S. Bhatia

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.14-24, Feb-2021

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  Abstract
  Biometric recognition is playing a vital role in various applications these days. Biometric traits like fingerprints, face, and iris are used for the identification of individuals. The matching is done by authenticating against a set of enrolled templates in the biometric system. These templates are vulnerable to attacks and any leakage of information or data can lead to a breach of privacy of individuals. In this paper, we review the existing techniques that have been used to secure biometric templates in a system. We also present a case study in which we propose a framework involving hashing of biometric templates using SHA-256 algorithm, which is followed by extraction of features and classification using deep convolutional neural networks.

  Key-Words / Index Term
  Template Protection, Biometrics, Hashing, Deep Learning, Convolutional Neural Networks

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  Citation
  Harshita Kukreja, Soumya Jain, Shefali Arora, M.P.S. Bhatia, "Privacy Enhancement in Biometric Systems by Template Protection using Deep Learning," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.14-24, 2021

• Open Access   Article

  Lecturer Attendance System Based on Near Field Communication Technology

  I Gede Sujana Eka Putra, Ni Luh Putu Labasariyani

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.25-31, Feb-2021

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  Abstract
  Attendance data is important to show the presence of lecturers and students attending lectures. In general, the lecturers` attendance is recorded on the attendance sheet manually, or inputted into a web application. However, there are several obstacles in its implementation so that in the end the lecturer attendance is carried out using a manual form where the academic administration staff needs to re-enter the lecturer attendance data into the application. Based on the description above, the authors propose research to design and develop attendance information system to record lecturer presence by implementing a near field communication based on radio frequency identification technology. The device used to record and read attendance data during lectures is an NFC Mi-fare card that is tapped into an NFC reader / writer device. The flow of research method begins with a literature study on NFC cards, observing the flow of lecture attendance processes and recording data into lecturer attendance sheets, analyzing database design, designing systems that are compatible with NFC readers and writers, designing user interface and continuing to develop the system. The result is attendance software which consists of master data, attendance system, verification and reporting modules. The results showed that the application of the NFC card is more practical for lecturers to attend lectures and the NFC card can be tapped into an NFC device at a maximum distance of up to 7 cm with a reading angle relative to the NFC reader / writer with a range of 00 to 300.

  Key-Words / Index Term
  Attendance System; NFC; RFID; Presence; Administration

  References
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  Citation
  I Gede Sujana Eka Putra, Ni Luh Putu Labasariyani, "Lecturer Attendance System Based on Near Field Communication Technology," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.25-31, 2021

• Open Access   Article

  Evolutionary Training of Binary Neural Networks by Evolution Strategy

  Hidehiko Okada

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.32-36, Feb-2021

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  Abstract
  A problem with deep neural networks is that the memory size for recording a trained model becomes large. A solution to this problem is to make the parameter values binary. A challenge for the binary neural networks is that they cannot be trained by the ordinary gradient-based optimization methods. This paper applies Evolution Strategy (ES), an instance of evolutionary algorithms, to the training of binary neural networks and evaluates its ability. The experimental results with the classification task revealed that ES could well optimize parameter values so that the trained model accurately classify both trained and untrained data, if the hidden layer included sufficient units. As the binary parameter value, {-1,1} was found to be significantly better than {0,1}.

  Key-Words / Index Term
  Evolutionary algorithm; Evolution strategy; Neural network; Network quantization; Neuroevolution.

  References
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  [15] S. Zhang, A. E. Choromanska, Y. LeCun, “Deep Learning with Elastic Averaging SGD,” Advances in Neural Information Processing Systems, pp.685-693, 2015.
  [16] I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning,” MIT Press, 2016.
  [17] M. Courbariaux, Y. Bengio, JP. David, “BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations,” Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15), pp.3123–3131, 2015.
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  [19] H. Qin, R. Gong, X. Liu, X. Bai, J. Song, N. Sebe, “Binary Neural Networks: A Survey,” Pattern Recognition, Vol.105, 107281, 2020.
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  Citation
  Hidehiko Okada, "Evolutionary Training of Binary Neural Networks by Evolution Strategy," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.32-36, 2021

• Open Access   Article

  Comparative Analysis of Selected Text Mining Classifiers

  Omokanye S.O., Abikoye O.C., Aro T.O., Akande H.B., Aregbesola K.M.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.37-42, Feb-2021

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  Abstract
  Text classification is a method of knowledge engineering in which expert level knowledge on classifying documents such that similar documents can be arranged in their respective categories. The field has been a growing area of research as there has been an increase in the availability of textual information. In this paper, three classifiers k-Nearest Neighbour (KNN), Multinomial Naïve Bayes (MNB), and Support Vector Machine (SVM) algorithms were compared based on accuracy and time taken to build a model using five datasets. Experimental results showed that with an increment in the number of training documents, the classification accuracy of the algorithms also increased and algorithms performed better in binary classification tasks than in multiclass classification tasks. The best accuracy of 98.3495% was recorded in SVM using SMS Spam collection dataset compared with other classifiers and datasets, for binary classification, the best classification accuracy of 98.3495% was obtained in SVM using SMS Spam collection dataset, and the best accuracy of 88.28% was obtained in SVM using Reuters 50-50 dataset. The lowest time of 0.01s which was also considered as the best time taken to build a model was recorded in MNB classifier.

  Key-Words / Index Term
  Naïve Bayes; Nearest Neighbor; Support Vector Machine; Text mining

  References
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  Citation
  Omokanye S.O., Abikoye O.C., Aro T.O., Akande H.B., Aregbesola K.M., "Comparative Analysis of Selected Text Mining Classifiers," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.37-42, 2021

• Open Access   Article

  Efficient Hybrid Load Balancing In Cloud Environment Using Dragonfly-Raven Roosting Algorithm

  Sandeep Nanda

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.43-47, Feb-2021

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  Abstract
  Cloud computing is the most emerging computing and is the interconnection of computers with servers the different major issues that arise in this computing are load balancing, fault tolerance and security. In load balancing technique the tasks are equally distributed among the servers or nodes. Task Scheduling assigns the different users tasks to the Virtual Machines for achieving different QoS parameters. In this paper, the Raven Roosting algorithm is combined with Dragonfly algorithm for improving the load between the computers and servers. This proposed algorithm minimizes the make span of the cloud system. I have compared our results with the Dragonfly algorithm and Raven Roosting based algorithm. The experimental result indicates that the DF-RRA based technique performs better in terms of load balancing and having a reduced make span. This proposed algorithm performs higher than the Raven Roosting algorithm and Dragonfly algorithm.

  Key-Words / Index Term
  Cloud computing, Load balancing, Task scheduling, Make span, Dragonfly Algorithm (DFA), Raven Roosting Algorithm (RRA)

  References
  [1] Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. In: Future Generation Computer Systems, vol. 25(6), pp. 599–616.
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  [13] S. Bilgaiyan, S.Sagnika and M.Das , a heuristic algorithm based on cat swarm optimization (CSO) for workflow scheduling in the cloud environment 4th IEEE International Advance Computing (IACC), pp.680-685, 2014
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  Citation
  Sandeep Nanda, "Efficient Hybrid Load Balancing In Cloud Environment Using Dragonfly-Raven Roosting Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.43-47, 2021

• Open Access   Article

  Using Tensor Processing Units to enhance the training of Convolutional Neural Networks in Multiclass Classification

  K.V.N. Rajesh, D. Lalitha Bhaskari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.48-55, Feb-2021

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  Citation

  Abstract
  Ayurveda is the ancient medical science of India. Many of the Ayurvedic medicines are plant-based. There is growing interest in Ayurveda nowadays and hence there is a need to make it more relevant to the current era by using modern technologies in Ayurveda. Identification of required Ayurvedic plants among a host of other plants is a problem that can be solved using computer vision-based technology. In this research work, classification of plants is done using Convolution Neural Networks (CNN) on the images and labels of respective leaves. A leaf image dataset for seven different Ayurvedic plants has been created as a part of this research work. Keras deep learning framework with TensorFlow as the backend is used for building the CNN model. The platform used for training this model was Google Colab which is available free of cost. As a part of this research work, Method and program are developed to load data from normal storage like Google drive, to TPU. After loading the image data to TPU, the TPU hardware acceleration is used for training the CNN model. In addition to a custom CNN model developed from scratch, pretrained image recognition models like DenseNet201, EfficientNetB7, InceptionV3, ResNet50V2, VGG19 and Xception are leveraged as a part of this research work. Image recognition accuracies ranging from 95% to 100% have been achieved using the mentioned CNN training methods for plant leaf image classification.

  Key-Words / Index Term
  Ayurvedic Plant Leaf Images, Custom Leaf Image Dataset, Computer Vision, Multiclass Classification, Convolution Neural Networks, Pre-trained image recognition models, Tensor Processing Unit

  References
  [1] Luna, R.G.d., Baldovino, R.G., Cotoco, E.A., Ocampo, A.L.P.d., Valenzuela, I.C., Culaba, A.B. and Dadios, E.P. “Identification of Philippine Herbal Medicine Plant Leaf Using Artificial Neural Network,” In the proceedings of 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila. pp. 1-8,2017.
  [2] Liu, W., Li, Z., Lin, J. and Liang, D. “The PSO-SVM-based Method of the Recognition of Plant Leaves,” In the proceedings of 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). pp. 1350-1355. 2017.
  [3] Beikmohammadi, A. and Faez, K., “Leaf Classification for Plant Recognition with Deep Transfer Learning,” In the proceedings of the 2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS,. pp. 21-26,2018.
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  [5] Liantoni, F., Perwira, R.I., Muharom, S., Firmansyah, R.A. and Fahruzi, A. “Leaf classification with improved image feature based on the seven moment invariant,” Journal of Physics: Conference Series, In the proceedings of 1st International Conference on Advance and Scientific Innovation , Medan, Indonesia IOP Publishing.23–24 April 2018.
  [6] Kayhan, G. and Ergün, E.”Medicinal and Aromatic Plants Identification Using Machine Learning Methods.” Balkan Journal of Electrical & Computer Engineering, 8(1), pp. 81-87, 2020.
  [7] Chompookham, T., Gonwirat, S., Lata, S. and Phiphiphatphaisit, S. “Plant Leaf Image Recognition using Multiple grid Based Local Descriptor and Dimensionality Reduction Approach,” ICISS 2020,In the Proceedings of the 2020 The 3rd International Conference on Information Science and System. pp. 72-77,2020.
  [8] Tang, Z. (2020) “Leaf image recognition and classification based on GBDT-probabilistic neural network,”Journal of Physics: Conference Series, In the Third International Conference on Physics, Mathematics and Statistics (ICPMS2020), Kunming, China IOP Publishing Ltd, May 20-22, 2020.
  [9] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich, “Going deeper with convolutions,” In the Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Boston,MA. pp. 1-9,2015.
  [10] Karen Simonyan and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition.”In the proceedings of International Conference on Learning Representations, 2015.
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  [12] F.Chollet “Xception: Deep Learning with Depthwise Separable Convolution,” In the proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 1800-1807.2017.
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  Citation
  K.V.N. Rajesh, D. Lalitha Bhaskari, "Using Tensor Processing Units to enhance the training of Convolutional Neural Networks in Multiclass Classification," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.48-55, 2021

• Open Access   Article

  A Next-Gen Power Generation Using Simulation And Machine Learning Forecasting

  Anurag Sinha, Ashutosh Kumar Sinha, Ankit Anand

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.56-65, Feb-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Renewable Energy in India is considered to be the foundation of the economy for all over this society and went to reach the end our economy of India depends upon. Also a critical factor in imaging sustainable Society renewable energy prominently depends upon the local environmental and ambiance conditions such as temperature rainfall in other ratios. For hydropower currently is the primary renewable source which is harmonizing to the electricity supply and its future participation is about to increase significantly.The appropriate forecasting of the energy management is very crucial issue for the available power management process. In this paper we have used several machine learning techniques for nominal forecasting of the energy produced by the several hydroelectricity power plants in India. Machine learning is considered to be a powerful tool for predicting the future nature of the data which is collected for the past history. Some machine learning taking and expecting the features and it will protect our take the decision for the future outcome. So in this paper we have used the previous data sets of the team for predicting the forecasting of the energy produced by the hydroelectric power plants. The manually operating hydro electric power plant turbine and generator of include some problem with the lowest speed and all the other elliptical and then deleted problems. The utmost power following system created by the most favorable load between the voltage and current produced by an Electromagnetic generator, in this paper we have used sensor system which is developed to measure the power originator in transformation characteristics between the rotational power through the automatic power generator and the turbine system. We have used the Adriano sensor which is embedded in the turbine and generator which will ultimately do all the functionality of the hydroelectric power plant.

  Key-Words / Index Term
  hydroelectric power plant, machine learning, SVM, ANN algorithm, sensors.

  References
  [1] K.-J. Chae, et al.”Reliable energy recovery in an existing municipal wastewater treatment plant with a flow-variable micro-hydropower” s Energy Convers. Manage., 101 (2015), pp. 681-688
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  [3] Luka Selak, Peter Butala, and Alojzij Sluga. “Condition monitoring and fault diagnostics for hydropower plants”. In: Computers in Industry 65 (2014), pp. 924–936
  [4] K. Hatalis, P. Pradhan, S. Kishore, and R.S Blum. “Multi-step forecasting of wave power using a nonlinear recurrent neural network”In Proceedings of the PES General Meeting — Conference Exposition, pages 1–5, National Harbor, MD, June 2014.
  [5] R.J. Hyndmann and A.B. Koehler. “Another look at measures of forecast accuracy” International Journal of Forecasting, 22:679–688, 2006.
  [6] E. Izgia, A. Oztopalb, B. Yerlib, M.K. Kaymakb, and A.D. Sahinb. “Determination of the representative time horizons for short-term wind power prediction by using artificial neural networks”. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36:1800–1809, 2014. [7] W.-Ch. Wanga, K.-W. Chauc, Ch.-T. Cheng, and L. Qiu. “A comparison of performance of several articial intelligence methods for forecasting monthly discharge time series”. Journal of Hydrology, 374:294–306, 2009.
  [8] A.K. Yadav, A.K.and Sachan.” Research and application of dynamic neural network based on reinforcement learning”. In Proceedings of the International Conference onInformation Systems Design and Intelligent Applications, Advances in Intelligent and Soft Computing, pages 931–942, Visakhapatnam, India, January 2012. Shpringer. [9] Doucoure, B.; Agbossou, K.; Cardenas, A. “Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data” Renew. Energy 2016, 92, 202–211.
  [10] N. Sharma, P. Sharma, D. Irwin and P. Shenoy, "Predicting solar generation from weather forecasts using machine learning", 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 528-533, 2011.
  [11] Elbatran, A. H., Yaakob, O. B., Yasser, M. Ahmed, Shabara, H. M. 2015. “Operation, Performance and Economic Analysis of Low Head MicroHydropower Turbines for Rural and Remote Areas”: A Review. Renewable and Sustainable Energy Reviews. 4340–50.
  [12] M. Rana, I. Koprinska and V. G. Agelidis, "Solar power forecasting using weather type clustering and ensembles of neural networks", International Joint Conference on Neural Networks (IJCNN), pp. 4962-4969, 2016.
  [16] A. Alfadda, R. Adhikari, M. Kuzlu and S. Rahman, "Hour-ahead solar PV power forecasting using SVR based approach", IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1-5, 2017.
  [13] T. Bøckman, S. Fleten, E. Juliussen, H. J. Langhammer, and I. Revdal, “Investment timing and optimal capacity choice for small hydropower projects,” European Journal of Operational Research, vol. 190, no. 1, pp. 255–267, 2008.
  [14] P. Coulibaly, F. Anctil, and B. Bobée, “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach,” Journal of Hydrology, vol. 230, no. 3-4, pp. 244–257, 2000.
  [15] Elizabeth Tomko emtomko, James Whyte “Development of Electricity Generation and Sensor System for a Hydropower Propagating Wave Turbine”.

  Citation
  Anurag Sinha, Ashutosh Kumar Sinha, Ankit Anand, "A Next-Gen Power Generation Using Simulation And Machine Learning Forecasting," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.56-65, 2021

• Open Access   Article

  EEG Signal Classification and Human Sensation Recognition Using Machine Learning Techniques

  Manini Monalisa Pradhan

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.1 , pp.66-71, Feb-2021

  Abstract

  Full-Text HTML

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  Citation

  Abstract
  Electroencephalogram (EEG) signals is generally contains enormous measures of huge number of data with uncountable classifications. The EEG signals turn ought more difficult amid assessing task if the captured data is over a long period. The powerful methodologies are important to secure the hidden and significant data delivered by the action in human cerebrum that covered inside the signals. In this manner, relevant strategies are produced to developed classify data. There are bunches of past researches and works related to the previously mentioned task, both feature extraction and classification have not been all around created in accomplishing more prominent precision. In this paper, methodologies were proposed for the emotions arrangement of EEG which can give high precision. The investigation of this thesis addresses and explores the accompanying issues: the best appropriate feature and full of emotion strategies for highlight extraction of EEG.

  Key-Words / Index Term
  EEG, PSD, SMO, RBF, KNN, BN, photoplethysmyograph

  References
  [1] M. Fatourechi, A. Bashashat. R.K. Ward, G.E Birch, “ EMG and EOG artifactsin brain Computer interface systems: A survey. Clininical Neurophysiology”, Vol.118 ,pp 480–494, 2007.
  [2] S. K. Hadjidimitriou, L. J. Hadjileontiadis,’ “Toward an EEG-based recognition of music liking using time-frequency analysis”, Biomedical Engineering, IEEE Transactions On,Vol.59, Isse.2, pp.3498-3510, 2012.
  [3] E. I. Konstantinidis, C. A. Frantzidis, C. Pappas, P. D Bamidis,“Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units”. Computer methods and programs in biomedicine, Vol 107, Issue.1, pp.16-27. 2012,
  [4] M. Murugappan, R. Nagarajan, S.Yaacob ,”Combining spatial filtering and wavelet transform for classifying human emotions using EEG Signals”, Journal of Medical and Biological Engineering, Vol.31, Issue.1, pp.45-51, 2011.
  [5] R. Khosrowabadi, H. C. Quek, A. Wahab, K. K. Ang, “EEG-based emotion recognition using self-organizing map for boundary detection”, 20th International Conference on Pattern,2010.
  [6] S. A. Hosseini, M. A. Khalilzadeh, M. B. Naghibi Sistani, V. Niazmand, “Higher order spectra analysis of EEG signals in emotional stress states”, 2nd International Conference on Information Technology and Computer Science, 2010.
  [7] Y H Liu, CT Wu, YH Kao, YT Chen,”Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine”, IEEE 35th Annual International Conference of the Engineering in Medicine and Biology Society(EMBC),2013.
  [8] Sohaib, Ahmad Tauseef, et al, “Evaluating classifiers for emotion recognition using EEG”, Foundations of Augmented Cognition, Springer Publication, Berlin Heberg, pp. 492-501, 2013..

  Citation
  Manini Monalisa Pradhan, "EEG Signal Classification and Human Sensation Recognition Using Machine Learning Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.66-71, 2021