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i-manager's Journal on Computer Science (JCOM)

Current Issue Vol. 12 Issue 3

Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code

Most Cited

Volume 10 Issue 3 September - November 2022

Research Paper

An Analysis on Combined Datasets for Fake News Detection through Machine Learning Approach

Khushbu Jaiswal* , Abhishek Bansal**

*-** Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India.

Jaiswal, K., and Bansal, A. (2022). An Analysis on Combined Datasets for Fake News Detection through Machine Learning Approach. i-manager’s Journal on Computer Science, 10(3), 1-6. https://doi.org/10.26634/jcom.10.3.19104

Abstract

Social media is a useful platform that facilitates the sharing of information and builds a virtual network among communities. This platform is sometimes useful to connect people and share useful information among friends and relatives. Nowadays, this platform is also used to share skills through short videos, transfer payments through the Unified Payments Interface (UPI), advertise and promote products to enhance businesses. However, social media platforms are connected through public networks, so a lot of hackers are connected to the network. The hackers want to steal personal information or manipulate the views of the users. Therefore, it applies various social engineering activities to gather personal information and spread a lot of fake news through various channels, apps, and social media pages. Therefore, it is a great challenge to detect fake news. Currently, many research communities are working to implement an algorithm that can detect fake news automatically based on an analysis of data. In an analysis of data, machine learning approaches such as regression, classification, and clustering methods may play an important role in detecting fake news from various datasets obtained from social media sites or the internet. In this paper, a deep analysis of combined datasets for fake news detection has been performed and this analysis is based on machine learning approaches. In addition, it compares the performance of machine learning models such as logistic regression, support vector machines, random forests, passive-aggressive classifiers, and decision trees.

References

[1]. Agarwal, V., Sultana, H. P., Malhotra, S., & Sarkar, A. (2019). Analysis of classifiers for fake news detection. Procedia Computer Science, 165, 377-383. https://doi.org/10.1016/j.procs.2020.01.035

[2]. Basak, A. (n.d. a). Covid_Fake_News: Constraint_Val. csv. Retrieved from https://github.com/diptamath/covid_fake_news/blob/main/data/Constraint_Val.csv

[3]. Basak, A. (n.d. b). Covid_Fake_News: Constraint_ Train. csv. Retrieved from https://github.com/diptamath/covid_ fake_news/blob/main/data/Constraint_Train.csv

[4]. Basak, A. (n.d. c). Covid_Fake_News: English_Test_ With_Labels.Csv. Retrieved from https://github.com/diptamath/covid_fake_news/blob/main/data/english_ test_with_labels.csv

[5]. Basak, A. (n.d. d). Covid_Fake_News: Constraint_Test. csv. Retrieved from https://github.com/diptamath/covid_fake_news/blob/main/data/Constraint_Test.csv

[6]. Jadhav, M. (n.d.). Real-Fake Job Post. Retrieved from https://www.kaggle.com/code/mranaljadhav/real-fakejob-post/data

[7]. Kaggle. (n.d. a). Fake News: Build a System to Identify Unreliable News Articles. Retrieved from https://www.kaggle.com/competitions/fake-news/data

[8]. Kaggle. (n.d. b). Natural Language Processing with Disaster Tweets: Predict which Tweets are about Real Disasters and which ones are not. Retrieved from https://www.kaggle.com/c/nlp-getting-started/data?select=train.csv

[9]. Parikh, S. B., & Atrey, P. K. (2018, April). Media-rich fake news detection: A survey. In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 436-441). IEEE. https://doi.org/10.1109/MIPR.2018.00093

[10]. Reis, J. C., Correia, A., Murai, F., Veloso, A., & Benevenuto, F. (2019). Supervised learning for fake news detection. IEEE Intelligent Systems, 34(2), 76-81. https://doi.org/10.1109/MIS.2019.2899143

[11]. Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797-806). https://doi.org/10.1145/3132847.3132877

[12]. Sharma, U., Saran, S., & Patil, S. M. (2020). Fake news detection using machine learning algorithms. International Journal of Engineering Research & Technology (IJERT), 9(3), 509-518.

[13]. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22-36. https://doi.org/10.1145/3137597.3137600

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Research Paper

An AI-Based Online Exam Proctoring Framework

Roli Jain* , Puja Gupta, Sahil Bansal*, Vichitra Singh****

*-**** Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India.

Jain, R., Gupta, P., Bansal, S., and Singh, V. (2022). An AI-Based Online Exam Proctoring Framework. i-manager’s Journal on Computer Science, 10(3), 7-12. https://doi.org/10.26634/jcom.10.3.19161

Abstract

In recent years, there has been an increase in online education. However, an adequate technique was not developed for online examinations. Some educational facilities have compiled assignments that students may copy and paste from the web, while others use remote proctoring, where a human proctor monitors what the students are doing online. Cheating in online tests remains common despite all the growth that has taken place in this field. This paper proposes an Artificial Intelligence (AI)-based system that can assist in the automatic detection of fraud in online tests. This method is both efficient and trustworthy. This system outperformed previous systems in experiments.

References

[1]. Abisado, M. B., Rodriguez, R. L., Arias Jr, A. R. V., Isip, C. M. M., Bungay, J. D. D., Cipriano, J. M. V., &Vea, L. A. (2019, September). Modeling filipino academic affect during online examination using machine learning. In Proceedings of the 20th Annual SIG Conference on Information Technology Education (pp. 167-167). https://doi.org/10.1145/3349266.3351387

[2]. Alessio, H. M., Malay, N., Maurer, K., Bailer, A. J., & Rubin, B. (2017). Examining the effect of proctoring on online test scores. Online Learning, 21(1), 146-161.

[3]. Gupta, P., & Kulkarni, N. (2013). An introduction of soft computing approach over hard computing. International Journal of Latest Trends in Engineering and Technology (IJLTET), 3(1), 254-258.

[4]. Gupta, P., Sharma, V., & Varma, S. (2021). People detection and counting using YOLOv3 and SSD models. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.11.562

[5]. Gupta, P., Shukla, M., Arya, N., Singh, U., & Mishra, K. (2022). Let the blind see: An aiiot-based device for realtime object recognition with the voice conversion. In Machine Learning for Critical Internet of Medical Things (pp. 177-198). Springer, Cham. https://doi.org/10.1007/978-3-030-80928-7_8

[6]. Ling, Z., Qing, X., Wei, H., & Yingcheng, L. (2017, October). Single view head pose estimation system based on SoC FPGA. In 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (pp. 369-374). IEEE. https://doi.org/10.1109/ICEMI.2017.8265822

[7]. Liu, X., Liang, W., Wang, Y., Li, S., & Pei, M. (2016, September). 3D head pose estimation with convolutional neural network trained on synthetic images. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 1289-1293). IEEE. https://doi.org/10.1109/ICIP.2016.7532566

[8]. Liu, X., Lu, H., & Li, W. (2010, September). Multimanifold modeling for head pose estimation. In 2010 IEEE International Conference on Image Processing (pp. 3277-3280). IEEE. https://doi.org/10.1109/ICIP.2010.5652540

[9]. Miller, A., Shoptaugh, C., & Wooldridge, J. (2011). Reasons not to cheat, academic-integrity responsibility, and frequency of cheating. The Journal of Experimental Education, 79(2), 169-184. https://doi.org/10.1080/00220970903567830

[10]. MotenJr, J., Fitterer, A., Brazier, E., Leonard, J., & Brown, A. (2013). Examining online college cyber cheating methods and prevention measures. Electronic Journal of E-learning, 11(2), 139-146.

[11]. Mounica, M. S., Manvita, M., Jyotsna, C., & Amudha, J. (2019, March). Low cost eye gaze tracker using web camera. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 79-85). IEEE. https://doi.org/10.1109/ICCMC.2019.8819645

[12]. Prathish, S., & Bijlani, K. (2016, August). An intelligent system for online exam monitoring. In 2016, International Conference on Information Science (ICIS) (pp. 138-143). IEEE. https://doi.org/10.1109/INFOSCI.2016.7845315

[13]. Sakurai, K., Yan, M., Tamura, H., & Tanno, K. (2016, October). Comparison of two techniques for gaze estimation system using the direction of eyes and head. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 002466-002471). IEEE. https://doi.org/10.1109/SMC.2016.7844609

[14]. Singh, U., & Gupta, P. (2022). Activity detection and counting people using mask-RCNN with bidirectional ConvLSTM. Journal of Intelligent & Fuzzy Systems, 43(5), 6505-6520. https://doi.org/10.3233/JIFS-220503

[15]. Wang, S., Wang, J., Peng, H., Gao, S., & He, D. (2016, December). A new calibration-free gaze tracking algorithm based on DE-SLFA. In 2016 8th International Conference on Information Technology in Medicine and Education (ITME) (pp. 380-384). IEEE.

[16]. Wise, S. L., & Plake, B. S. (1989). Research on the effects of administering tests via computers. Educational Measurement: Issues and Practice, 8(3), 5-10. https://doi.org/10.1111/j.1745-3992.1989.tb00324.x

[17]. Wu, Z., Rajendran, S., Van As, T., Badrinarayanan, V., & Rabinovich, A. (2019, October). Eyenet: A multi-task deep network for off-axis eye gaze estimation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (pp. 3683-3687). IEEE. https://doi.org/10.1109/ICCVW.2019.00455

[18]. Yağci, M., & Ünal, M. (2014). Designing and implementing an adaptive online examination system. Procedia-Social and Behavioral Sciences, 116, 3079-3083. https://doi.org/10.1016/j.sbspro.2014.01.711

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Review Paper

SVM-Based Stock Market Price Prediction Methods: An Advanced Review

Vijay Kumar Vishwakarma* , Narayan P. Bhosale**

*-** Department of Computer Science, Indira Gandhi National Tribal University (A Central University), Amarkantak, Madhya Pradesh, India.

Vishwakarma, V. K., and Bhosale, N. P. (2022). SVM-Based Stock Market Price Prediction Methods: An Advanced Review. i-manager’s Journal on Computer Science, 10(3), 13-20. https://doi.org/10.26634/jcom.10.3.19183

Abstract

This paper offers a concise analysis of the strategies currently in use for stock price prediction by retail investors. The price may increase or decrease according to the quarterly results, financial news flow, technical behavior, or market sentiment resulting from recent developments worldwide. This paper discussed the accuracy of many proposed approaches and methodologies for predicting stock price movement. The Support Vector Machine (SVM) is the foundation of the approaches, with additional parameters and variables.

References

[1]. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 144-152). https://doi.org/10.1145/130385.130401

[2]. Chen, H. M., Chang, H. H., Fang, S. W., & Teng, W. G. (2019, June). Options trading and hedging strategies based on market data analytics. In 9th International Conference on Computer Science and Information Technology (CCSIT 2019), 9(8), 31–43. https://doi.org/10.5121/csit.2019.90804

[3]. Grigoryan, H. (2016). A stock market prediction method based on support vector machines (SVM) and independent component analysis (ICA). Database Systems Journal, 7(1), 12-21.

[4]. Grigoryan, H. (2017, May). Stock market trend prediction using support vector machines and variable selection methods. In 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017) (pp. 210-213). Atlantis Press.

[5]. Gururaj, V., Shriya, V. R., & Ashwini, K. (2019). Stock market prediction using linear regression and support vector machines. International Journal of Applied Engineering Research, 14(8), 1931-1934.

[6]. Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4(3), 183-201. https://doi.org/10.1016/j.jfds.2018.04.003

[7]. Heo, J., & Yang, J. Y. (2016). Stock price prediction based on financial statements using SVM. International Journal of Hybrid Information Technology, 9(2), 57-66. http://doi.org/10.14257/ijhit.2016.9.2.05

[8]. Puspitasari, D. A., & Rustam, Z. (2018, October). Application of SVM-KNN using SVR as feature selection on stock analysis for Indonesia stock exchange. In AIP Conference Proceedings, 2023(1), 020207. AIP Publishing LLC. https://doi.org/10.1063/1.5064204

[9]. Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitons & Fractals, 85, 1-7. https://doi.org/10.1016/j.chaos. 2016.01.004

[10]. Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717. https://doi.org/10.3390/electronics10212717

[11]. Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 1-12. https://doi.org/ 10.1186/s40854-019-0131-7

[12]. Shubhrata, D. M., Kaveri, V. D., Pranit, R. T, Bhavana, Y. S., & Chate, P. J. (2016). Stock market prediction and analysis using Naïve Bayes. International Journal on Recent and Innovation Trends in Computing and Communication, 4(11), 121-124.

[13]. Tripathy, N. (2019, November). Stock price prediction using support vector machine approach. International Academic Conference on Management and Economics, 44-59. https://doi.org/10.33422/conferenceme.2019.11.641

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Review Paper

Techniques for Detecting Video Shot Boundaries: A Review

Sharma. S* , Malakar. P**

*-** Department of Information Technology & Computer Science, Dr. CV Raman University, Chhattisgarh, Kota, Bilaspur, India.

Sharma, S., and Malakar, P. (2022). Techniques for Detecting Video Shot Boundaries: A Review. i-manager’s Journal on Computer Science, 10(3), 21-26. https://doi.org/10.26634/jcom.10.3.19111

Abstract

Due to the Corona Virus Diseases (COVID-19) pandemic, education is completely dependent on digital platforms, so recent advances in technology have made a tremendous amount of video content available. Due to the huge amount of video content, content-based information retrieval has become more and more important. Video content retrieval, just like information retrieval, requires some pre-processing such as indexing, key frame selection, and, most importantly, accurate detection of video shots. This gives the way for video information to be stored in a manner that will allow easy access. Video processing plays a vital role in many large applications. The applications required to perform the various manipulations on video streams (as on frames or say shots). The high definition of video can take a lot of memory to store, so compression techniques are huge in demand. Also, object tracking or object identification is an area where much considerable research has taken place and it is in progress.

References

[1]. Abdulhussain, S. H., Ramli, A. R., Saripan, M. I., Mahmmod, B. M., Al-Haddad, S. A. R., & Jassim, W. A. (2018). Methods and challenges in shot boundary detection: a review. Entropy, 20(4), 214. https://doi.org/10.3390/e20040214

[2]. Bi, C., Yuan, Y., Zhang, J., Shi, Y., Xiang, Y., Wang, Y., & Zhang, R. (2018). Dynamic mode decomposition based video shot detection. IEEE Access, 6, 21397-21407. https://doi.org/10.1109/ACCESS.2018.2825106

[3]. Hannane, R., Elboushaki, A., Afdel, K., Naghabhushan, P., & Javed, M. (2016). An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram. International Journal of Multimedia Information Retrieval, 5(2), 89-104. https://doi.org/10.1007/s13735-016-0095-6

[4]. Heng, W.J., Ngan, K.N.: An Object-Based Shot Boundary Detection Using Edge Tracing and Tracking. Journal of Visual Communication and Image Representation 12, 217–239 (2001)

[5]. Janwe, N. J., & Bhoyar, K. K. (2013, December). Video shot boundary detection based on JND color histogram. In 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013) (pp. 476-480). IEEE.

[6]. Karpagavalli, S., Balamurugan, V., & Kumar, S. R. (2020, February). A novel hybrid keypoint detection algorithm for gradual shot boundary detection. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-5). IEEE. https://doi.org/10.1109/ic-ETITE47903.2020.343

[7]. Wu, Z., & Xu, P. (2014). A fast gradual shot boundary detection method based on SURF. In Practical Applications of Intelligent Systems (pp. 699-706). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54927-4_66

[8]. Lu, Z.M., Shi, Y.: Fast Video Shot Boundary Detection Based on SVD and Pattern Matching. IEEE Transactions on Image Processing 22(12), 5136–5145 (2013)

[9]. Yi, H., Pengzhou, Z., & Yanfeng, W. (2012, November). Adaptive threshold based video shot boundary detection framework. In 2012 International Conference on Image Analysis and Signal Processing (pp. 1-5). IEEE. https://doi.org/10.1109/IASP.2012.6425020

[10]. Yong, C., Xu, Y., & De, X. (2002, October). A method for shot boundary detection with automatic threshold. In 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM'02. Proceedings. (Vol. 1, pp. 582-585). IEEE. https://doi.org/10.1109/TENCON.2002.1181342

[11]. Zheng, Y., & Zhang, Y. (2016, October). Abrupt shot boundary detection with combined features and SVM. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 409-413). IEEE. https://doi.org/10.1109/CompComm.2016.7924733

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Review Paper

A Review on Emotional Speech Databases

Youddha Beer Singh*

Department of Computer Science and Information Technology, KIET Group of Institutions, Delhi-NCR Ghaziabad, India.

Singh, Y. B. (2022). A Review on Emotional Speech Databases. i-manager’s Journal on Computer Science, 10(3), 27-33. https://doi.org/10.26634/jcom.10.3.19103

Abstract

Due to its numerous practical applications, human emotion recognition from speech is now a challenging and demanding research subject for scientists. The Speech databases, speech features, and classifiers are the important factors for recognizing emotions from speech. The availability of suitable emotional speech databases is the first step for Speech Emotion Recognition (SER). This paper presents a comprehensive literature review of emotional speech databases. The availability of appropriate emotional speech databases in all emotions and languages are summarized. A total of 26 papers for the emotional speech database have been reviewed. It determines which speech databases are often used and also identifies popular speech databases and the languages in which the majority of the databases are available.

References

[1]. Adigwe, A., Tits, N., Haddad, K. E., Ostadabbas, S., & Dutoit, T. (2018). The emotional voices database: Towards controlling the emotion dimension in voice generation systems. arXiv preprint arXiv:1806.09514. https://doi.org/10.48550/arXiv.1806.09514

[2]. Bansal, S., & Dev, A. (2013, November). Emotional Hindi speech database. In 2013 International Conference Oriental COCOSDA Held Jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE) (pp. 1-4). IEEE. https://doi.org/10.1109/ICSDA.2013.6709867

[3]. Bao, W., Li, Y., Gu, M., Yang, M., Li, H., Chao, L., & Tao, J. (2014, October). Building a Chinese natural emotional audio-visual database. In 2014 12th International Conference on Signal Processing (ICSP) (pp. 583-587). IEEE. https://doi.org/10.1109/ICOSP.2014.7015071

[4]. Batliner, A., Buckow, J., Niemann, H., Nöth, E., & Warnke, V. (2000). The prosody module. In Verbmobil: Foundations of Speech-To-Speech Translation (pp. 106-121). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04230-4_8

[5]. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005, September). A database of German emotional speech. In Interspeech, 5, 1517-1520.

[6]. Busso, C., Bulut, M., Lee, C. C., Kazemzadeh, A., Mower, E., Kim, S., ...& Narayanan, S. S. (2008). IEMOCAP: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42(4), 335-359. https://doi.org/10.1007/s10579-008-9076-6

[7]. Cao, H., Cooper, D. G., Keutmann, M. K., Gur, R. C., Nenkova, A., & Verma, R. (2014). Crema-d: Crowdsourced emotional multimodal actors dataset. IEEE Transactions on Affective Computing, 5(4), 377-390. https://doi.org/10.1109/TAFFC.2014.2336244

[8]. Chen, J., Wang, C., Wang, K., Yin, C., Zhao, C., Xu, T., ...& Yang, T. (2021). HEU Emotion: a large-scale database for multimodal emotion recognition in the wild. Neural Computing and Applications, 33(14), 8669-8685. https://doi.org/10.1007/s00521-020-05616-w

[9]. Costantini, G., Iaderola, I., Paoloni, A., &Todisco, M. (2014). EMOVO corpus: an Italian emotional speech database. In International Conference on Language Resources and Evaluation (LREC 2014) (pp. 3501-3504).

[10]. El Ayadi, M., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition, 44(3), 572-587. https://doi.org/10.1016/j.patcog.2010.09.020

[11]. Grimm, M., Kroschel, K., & Narayanan, S. (2008, June). The Vera am mittaggerman audio-visual emotional speech database. In 2008 IEEE International Conference on Multimedia and Expo (pp. 865-868). IEEE. https://doi.org/10.1109/ICME.2008.4607572

[12]. Haq, S., & Jackson, P. J. (2011). Multimodal emotion recognition. In Machine Audition: Principles, Algorithms and Systems (pp. 398-423). IGI Global. https://doi.org/10.4018/978-1-61520-919-4.ch017

[13]. Khanh, T. L. B., Kim, S. H., Lee, G., Yang, H. J., & Baek, E. T. (2021). Korean video dataset for emotion recognition in the wild. Multimedia Tools and Applications, 80(6), 9479-9492. https://doi.org/10.1007/s11042-020-10106-1

[14]. Koolagudi, S. G., Maity, S., Kumar, V. A., Chakrabarti, S., & Rao, K. S. (2009, August). IITKGP-SESC: speech database for emotion analysis. In International Conference on Contemporary Computing (pp. 485-492). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_46

[15]. Li, Y., Tao, J., Chao, L., Bao, W., & Liu, Y. (2017). CHEAVD: a Chinese natural emotional audio–visual database. Journal of Ambient Intelligence and Humanized Computing, 8(6), 913-924. https://doi.org/10.1007/s12652-016-0406-z

[16]. Livingstone, S. R., & Russo, F. A. (2018). The ryerson audio-visual database of emotional speech and song (ravdess): a dynamic, multimodal set of facial and vocal expressions in North American English. Plos One, 13(5), e0196391. https://doi.org/10.1371/journal.pone.0196391

[17]. Lubis, N., Gomez, R., Sakti, S., Nakamura, K., Yoshino, K., Nakamura, S., & Nakadai, K. (2016, May). Construction of Japanese audio-visual emotion database and its application in emotion recognition. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 2180-2184).

[18]. Martin, O., Kotsia, I., Macq, B., & Pitas, I. (2006, April). The eNTERFACE'05 audio-visual emotion database. In 22nd International Conference on Data Engineering Workshops (ICDEW'06) (pp. 8-8). IEEE. https://doi.org/10.1109/ICDEW.2006.145

[19]. Meftah, A. H., Qamhan, M. A., Seddiq, Y., Alotaibi, Y. A., & Selouani, S. A. (2021). King saud university emotions corpus: Construction, analysis, evaluation, and comparison. IEEE Access, 9, 54201-54219. https://doi.org/10.1109/ACCESS.2021.3070751

[20]. Parada-Cabaleiro, E., Costantini, G., Batliner, A., Baird, A., & Schuller, B. (2018). Categorical vs dimensional perception of Italian emotional speech. Interspeech, 3638-3642. https://doi.org/10.5281/zenodo.1326428.

[21]. Parada-Cabaleiro, E., Costantini, G., Batliner, A., Schmitt, M., & Schuller, B. W. (2020). DEMoS: An Italian emotional speech corpus. Language Resources and Evaluation, 54(2), 341-383. https://doi.org/10.1007/s10579-019-09450-y

[22]. Pichora-Fuller, M. K., & Dupuis, K. (2020). Toronto emotional speech set (TESS). Scholars Portal Dataverse, 1, 2020. https://doi.org/10.5683/SP2/E8H2MF

[23]. Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2018). Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508. https://doi.org/10.48550/arXiv.1810.02508

[24]. Rambabu, B., Botsa, K. K., Paidi, G., & Gangashetty, S. V. (2020, May). IIIT-H TEMD semi-natural emotional speech database from professional actors and non actors. In Proceedings of the 12th Language Resources and Evaluation Conference (pp. 1538-1545).

[25]. Ververidis, D., & Kotropoulos, C. (2006). Emotional speech recognition: Resources, features, and methods. Speech Communication, 48(9), 1162-1181. https://doi.org/10.1016/j.specom.2006. 04.003

[26]. Williams, C. E., & Stevens, K. N. (1972). Emotions and speech: Some acoustical correlates. The Journal of the Acoustical Society of America, 52(4B), 1238-1250. https://doi.org/10.1121/1.1913238

i-manager's Journal on Computer Science (JCOM)

Current Issue Vol. 12 Issue 3

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Volume 10 Issue 3 September - November 2022

An Analysis on Combined Datasets for Fake News Detection through Machine Learning Approach

Khushbu Jaiswal* , Abhishek Bansal**

*-** Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India.

Jaiswal, K., and Bansal, A. (2022). An Analysis on Combined Datasets for Fake News Detection through Machine Learning Approach. i-manager’s Journal on Computer Science, 10(3), 1-6. https://doi.org/10.26634/jcom.10.3.19104

Abstract

References

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An AI-Based Online Exam Proctoring Framework

Roli Jain* , Puja Gupta**, Sahil Bansal***, Vichitra Singh****

*-**** Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India.

Jain, R., Gupta, P., Bansal, S., and Singh, V. (2022). An AI-Based Online Exam Proctoring Framework. i-manager’s Journal on Computer Science, 10(3), 7-12. https://doi.org/10.26634/jcom.10.3.19161

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SVM-Based Stock Market Price Prediction Methods: An Advanced Review

Vijay Kumar Vishwakarma* , Narayan P. Bhosale**

*-** Department of Computer Science, Indira Gandhi National Tribal University (A Central University), Amarkantak, Madhya Pradesh, India.

Vishwakarma, V. K., and Bhosale, N. P. (2022). SVM-Based Stock Market Price Prediction Methods: An Advanced Review. i-manager’s Journal on Computer Science, 10(3), 13-20. https://doi.org/10.26634/jcom.10.3.19183

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Techniques for Detecting Video Shot Boundaries: A Review

Sharma. S* , Malakar. P**

*-** Department of Information Technology & Computer Science, Dr. CV Raman University, Chhattisgarh, Kota, Bilaspur, India.

Sharma, S., and Malakar, P. (2022). Techniques for Detecting Video Shot Boundaries: A Review. i-manager’s Journal on Computer Science, 10(3), 21-26. https://doi.org/10.26634/jcom.10.3.19111

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A Review on Emotional Speech Databases

Youddha Beer Singh*

Department of Computer Science and Information Technology, KIET Group of Institutions, Delhi-NCR Ghaziabad, India.

Singh, Y. B. (2022). A Review on Emotional Speech Databases. i-manager’s Journal on Computer Science, 10(3), 27-33. https://doi.org/10.26634/jcom.10.3.19103

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Roli Jain* , Puja Gupta, Sahil Bansal*, Vichitra Singh****