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Natural Language Processing is an active and emerging field of research in the computer sciences. Within it is the subfield of text simplification which is aimed towards teaching the computer the so far primarily manual task of simplifying text, efficiently. While handcrafted systems using syntactic techniques were the first simplification systems, Recurrent Neural Networks and Long Short Term Memory networks employed in seq2seq models with attention were considered state-of-the-art until very recently when the transformer architecture which did away with the computational problems that plagued them. This paper presents our work on simplification using the transformer architecture in the process of making an end-to-end simplification system for linguistically complex reference books written in English and our findings on the drawbacks/limitations of the transformer during the same. We call these drawbacks as the Fact Illusion Induction, Named Entity Problem and Deep Network Problem and try to theorize the possible reasons for them.

Artificial Intelligence, Natural Language Processing, Neural Networks, Text Simplification, Transformer

[1] Y. Goldberg, G. Hirst, ?Neural Network Methods in Natural Language Processing?, Morgan & Claypool Publishers, USA, 2017. ISBN no. 9781627052955
[2] A. Siddharthan, ?A Survey of Research on Text Simplification?, ITL - International Journal of Applied Linguistics, Vol.165, No.2, pp.259-298, 2014.
[3] M. Shardlow, ?A Survey of Automated Text Simplification.?, International Journal of Advanced Computer Science and Applications, Vol.4, No.1, pp. 58?70, 2014.
[4] S. Wubben, E. Krahmer, A. van den Bosch, ?Sentence Simplification by Monolingual Machine Translation?, In the Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Republic of Korea, pp.1015-1024, 2012.
[5] T. Vu, B. Hu, T. Munkhdalai, H. Yu, ?Sentence Simplification with Memory-augmented Neural Networks?, In the Proceedings of the NAACL-HLT, USA, pp.79-85, 2018.
[6] W. Coster, D. Kauchak, ?Simple English Wikipedia: A New Text Simplification Task.?, In the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, USA, pp.665-669, 2011.
[7] Z. Zhu, D. Bernhard, I. Gureych, ?A Monolingual Tree-based Translation Model for Sentence Simplification?, In the Proceedings of the 23rd International Conference on Computational Linguistics, China, pp.1353-1361, 2010.
[8] S. Nisioi, S. Stajner, S. P. Ponzetto, L. P. Dinu, ?Exploring Neural Text Simplification Models?, In the Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Canada, pp.85-91, 2017.
[9] A. Vaswani et al, ?Attention is all you Need?, In the Proceedings of the 31st Conference on Neural Information Processing Systems, USA, pp.5998-6008, 2017.
[10] S. Zhao, R. Meng, D. He, S. Andi, P. Bamabang, ?Integrating Transformer and Paraphrase Rules for Sentence Simplification?, In the Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Belgium, pp.3164-3173, 2018.

K. Mehta, H. Chodvadiya, S.R. Sankhe, "Simplification with the Transformer - Its Drawbacks," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.1-5, 2020.

In this age of growing digitization, many of our day to day activities are being transformed with the help of the internet and everything is now available online. With the advent of the internet, nowadays online transactions have become an important and necessary part of our lives. As the number of transactions are increasing, the number of fraudulent transactions are also increasing rapidly. To reduce fraudulent transactions, machine learning algorithms like Local Outlier Factor and Isolation Forest are discussed in this paper. An online dataset is used to implement and test these algorithms. Finally with comparative analysis we tried to conclude which algorithm works better.

Credit Card, Fraud, Machine Learning, Isolation Forest, Local Outlier Factor

[1] Vaishnavi Nath Dornadula, Geetha S, ?Credit Card Fraud Detection Using Machine Learning Algorithms?, International Conference on Recent Trends in Advanced Computing 2019
[2] Samaneh Sorournejad, Zahra Zojaji, Reza Ebrahimi Atani, Amir Hassan Monadjemi, ?A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective?
[3] Ramyashree K, Janaki K, Keerthana S, B. V. Harshita, Harshita Y. V, ?A Hybrid for Credit Card Fraud Detection Using Machine Learning Algorithm?, International Journal of Recent Technology And Engineering, Volume-7, Issue-6S4, April 2019
[4] Heta Naik, Prashasti Kanikar, ?Credit Card Fraud Detection Based on MAchine Learning Algorithms?, International Journal of Computer Applications, Volume 182 - No. 44, March 2019
[5] Suresh K. Shirgave, Chetan J. Awati, Rashmi More, Sonam S. Patil, ?A Review on Credit Card Fraud Detection Using Machine Learning?, International Journal of Scientific and Technology Research, Volume 8, Issue 10, October 2019
[6] S. P. Maniraj, Aditya Saini, Swarna Deep Sarkar, Shadab Ahmed, ?Credit Card Fraud Detection Using Machine Learning and Data Science?, International Journal of Engineering Research and Technology, Volume 8, Issue 9, September 2019
[7] Laxmi S. V. S.S, Selvani Deepthi Kavila, ?Machine Learning for Credit Card Fraud Detection System?, International Journal of Applied Engineering Research, Volume 13, Number 24(2018)

Jerin Ignatious,Yogita Kulkarni, Shruti Bari, Deepali Naglot, "Comparative Analysis of Machine Learning Algorithms for Credit Card Fraud Detection," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.6-9, 2020.

Computer vision has been an area of interest for engineers and scientists who have been spearheading in the field of artificial intelligence from the late 1960s as it was very essential to give machines or robots the power of visualizing objects and activities around them like the human visual system. The ability to visualize 2-Dimensional images and extracting features from them can be utilised for developing various applications. The involvement of deep learning has been successful in bolstering the field of computer vision even further. The abundance of images in today`s digital world and the amount of information contained in them have made them a very valuable and research worthy data item. A deep learning-based image caption generator model can incorporate the areas of natural language processing and computer vision with deep learning to give a solution in which the machine can extract features from an image and then describe those features in a natural language. Thus, explaining the contents of the image in a human-readable format. This model has various applications ranging from social causes like being an aid to visually impaired to enhancing search experience of users over the web. This paper analyses the various state-of-the-art work in the field of image processing, computer vision and deep learning and presents a deep learning model that generates captions describing the images given as input to the system.

Image caption, Recurrent Neural Networks, Feature Extraction, Image Description

[1] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, Y. Bengio. ?Show, attend and tell: Neural image caption generation with visual attention? In International conference on machine learning, pp. 2048-2057, 2015.
[2] M. Tanti, A. Gatt and K.P. Camilleri. "What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?" arXiv preprint arXiv:1708.02043, 2017.
[3] R. Bernardi, R. Cakici, D. Elliott, A. Erdem, E. Erdem, N. Ikizler-Cinbis, F. Keller, A. Muscat, B. Plank. ?Automatic description generation from images: A survey of models, datasets, and evaluation measures?, Journal of Artificial Intelligence Research, Vol. 55, pp. 409-442, 2016.
[4] O. Vinyals, A. Toshev, S. Bengio, D. Erhan. ?Show and tell: A neural image caption generator?, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3156-3164, 2015.
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[6] S. Li, G. Kulkarni, T. Berg, A. Berg, Y. Choi. ?Composing simple image descriptions using web-scale n-grams?, IN Proceedings of the Fifteenth Conference on Computational Natural Language Learning. Association for Computational Linguistics, pp. 220-228, 2011.
[7] R. Kiros, R. Salakhutdinov, R. Zemel. ?Multimodal neural language model?, In International conference on machine learning, p. 595-603, 2014.
[8] Y. Yang, C. Teo, H. Daum? III, Y. Aloimonos. ?Corpus-guided sentence generation of natural images?, In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 444-454, 2011.
[9] W. Zaremba, I. Sutskever, O. Vinyals. ?Recurrent neural network regularization?, arXiv preprint arXiv:1409.2329, 2014.
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[11] B. Yao, X. Yang, L. Lin, M. Lee, S. Zhu. ?I2T: Image parsing to Text Description?, In Proceedings of the IEEE, pp. 1485-1508, 2010.
[12] N. Kumar, D. Vigneswari, A. Mohan, K. Laxman, J. Yuvaraj. ?Detection and Recognition of Objects in Image Caption Generator System: A Deep Learning Approach?, In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 107-109, 2019.
[13] S. Shabir, S. Arafat. ?An image conveys a message: A brief survey on image description generation?, In 2018 1st International Conference on Power, Energy and Smart Grid (ICPESG), pp. 1-6, 2018.
[14] J. Li, Y. Wong, Q. Zhao, M. Kankanhalli. ?Video Storytelling: Textual Summaries for Events?, IEEE Transactions on Multimedia, Vol. 22, Issue. 2, pp. 554-565, 2019.
[15] X. Li, S. Jiang. ?Know more say less: Image captioning based on scene graphs?, IEEE Transactions on Multimedia, Vol 21, Issue 8, pp. 2117-2130, 2019.
[16] S. Kavitha, A. Senthil Kumar, "Long Short-Term Memory Recurrent Neural Network Architectures", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5 Issue 3, pp. 390-394, May-June 2019.
[17] Anitha Nithya R, Saran A , Vinoth R, "Adaptive Resource Allocation and Provisioning in MultiService Cloud Environments ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5 Issue 2, pp. 382-387, March-April 2019.

P. Aishwarya Naidu, Satvik Vats, Gehna Anand, Nalina V., "A Deep Learning Model for Image Caption Generation," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.10-17, 2020.

In past decade, Tumor is one of the dangerous diseases in the world causing death of many people. MRI is one of the imaging technique which is widely used for tumor detection and classification. Also there are various methods for detection of brain tumor other than LIPC . Convolution neural network(CNN) is used in convolving a signal or an image with kernels to obtain feature maps. The image processing techniques such as equalized image, feature extraction and histogram equalization have been developed for extraction of the tumor in the MRI images of the cancer affected patients. Support Vector Machine(SVM) algorithm that works on structural risk minimization to classify the images. The SVM algorithm is applied to MRI images for the tumor extraction and a Simulink model is developed for the tumor classification function.

Brain tumor, CNN, Faster RCNN, classification, tumor detection

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[3]. Kumar, P.; Kumar B.V.; ?Brain Tumor MRI Segmentation and Classification Using Ensemble Classifier?, International Journal of Recent Technology and Engineering, vol:8, 2019, pp: 244-252
[4]. Kaur, R.; Doegar, A.; ?Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques?, International Journal of Innovative Technology and Exploring Engineering, vol: 8, 2019, pp: 59-66
[5]. Bansal, S.; Kaur, S.; Kaur, N.; ?Enhancement in Brain Image Segmentation using Swarm Ant Lion Algorithm?, International Journal of Innovative Technology and Exploring Engineering, vol: 8, 2019, pp: 1623-1628
[6]. Kumar, S.; Dabas, C.; Godara, S.; ?Classification of Brain MRI Tumor Images: A Hybrid Approach?, Information Technology and Quantitative Management, vol: 122, 2017, pp: 510-517
[7]. Dharnia, S.; Wasson, V.; ?An Automated Brain Tumour Boundary Detection using Region based Segmentation Technique along with SVM Classifier?, International Journal of Engineering and Advanced Technology, vol: 8, 2019, pp: 2523-2531
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[13]. B.Ramasubramanian, G. Praphakar, S. Murugeswari, ? A Novel Approach for Content Based Microscopic Image Retrieval system Using Decision Tee Algorithm?, International journal of scientific& engineering research, Vol. 4, No 6, pp 584-588, 2013.
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[15]. Hashem Kalbkhani, Mahrokh G Shayesteh, BehroozZalivargahan ?Robust algorithm for Brain Magnetic Resonance Image Classification based on GARCH variances Series?, ELSEVIER Biomedical Signal Processing and Control 8(2013) 909-919
[16]. Sandeep Chaplot , L.M. Patnaik , N.R. Jagannathan, ?Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural Network?, Elsevier Journal on Biomedical Signal Processing and Control, Vo.1, No 1,pp 86 -92 ,2006.
[17]. Z. Iscan, Z. DokurandT. Olmez, ?Tumor detection by using Zernike moments on segmented magnetic resonance brain images?, Elsevier Journal of Expert system and Application, Vol. 37, No 3, pp 2540-2549, 2010.
[18]. ShenFurao, Tomotaka Ogura, Osamu Hasegawa, ?An Enhanced Self Organizing Incremental Neural network For Online Unsupervised learning?, Elsevier Journal on Neural Network, Vol. 20, No 8, pp 893-903, 2007.
[19]. Monika Jain, Shivanky Jaiswal, Sandeep Maurya, Mayank Yadav ? A Novel Approach for the Detection & Analysis of Brain Tumor,? International Journal of Emerging Technology and Advanced Engineering, vol. 5, Issue 4, pp. 54?59, 2015.
[20]. R. S. RajKumar and G. Niranjana, ?Image Segmentation and Classification of MRI Brain Tumor Based on Cellular Automata and Neural Networks,? IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 1, March 2013
[21]. KetanMachhale, HariBabuNandpuru , VivekKapur and LaxmiKosta, ?MRI Brain Cancer Classification Using Hybrid Classifier (SVM-KNN),? International Conference on Industrial Instrumentation and Control (ICIC), pp.60-65, 2015.
[22]. Padma Nanda Gopal &R.Sukanesh,? wavelet stat istical feature based segmentat ion and classificat ion of brain computed tomography images?IET Image P rosess Vol7 pp 25 -32 2013
[23]. KailashSinha and G. R. Sinha, ?Efficient Segmentation Methods for Tumor Detection in MRI Images,? IEEE Student?s Conference on Electrical, Electronics and Computer Science, pp.1-6, 2014.
[24]. Pranita Balaji Kanade, Prof. P.P. Gumaste, ?Brain tumor detection using mri images,? International journal of innovative research in electrical, electronics, instrumentation and control engineering, vol. 3, issue 2, pp.146-150, february 2015.

Navdeep Kaur, Rekha Bhatia, "Development of a Faster Region Based Convolution Neural Network technique for brain image classification," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.18-24, 2020.

Psychological wellness influence a noteworthy level of the world?s population every year. Stress is humans` response to various types of desires or threats. This response, when working properly, can help us to stay focused, energized and intellectually active, but if it is out of proportion, it can certainly be harmful leading to depression, anxiety, hypertension and a host of threatening disorders. The work has demonstrated the utility of online social information for contemplating despondency; be that as it may, there have been limited assessments of other mental well-being conditions. Cyberspace is a huge area for people to post anything and everything that they experience in their day-to-day lives. It can be used as a very effective tool in determining the stress levels of an individual based on the posts and updates shared by him/her. This is a proposal for a website which takes the username of the subject as an input, scans and analyses the subject`s profile by performing sentiment analysis and gives out results. These results suggest the overall stress levels of the person and give an overview of his/her mental and emotional state.

Psychological Stress Detection;CNN;NLTK;RELU;TFIDF

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[2] Shaikha Hajera, Mohammed Mahmood Adi, ?Psychological Stress Detection from Social Media using Novelty Hybrid Model?, IJCSE, vol.6,issue.8, 2018.
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S. Amrita, Jobin Joseph, Rona Shaji, Athul Prasad, Rahul Gopal, "E-Stress Detector," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.25-29, 2020.

The growth of social networking sites lead to increase in number of users and the amount of time spent by the users on these sites. In this era of internet, human expresses their emotions, sentiments and feelings via text, comments or tweets. People share their thoughts, feelings, experiences and opinions according to their observation and understanding. Emotion is an appearance of human behavior and plays an important role in human computer interaction. To extract the emotion behind this textual data we have proposed a model, emotion recognition from text. Our method detects emotion from a text-input by using deep learning algorithm Long Short Term Memory (LSTM). Emotions such as anger, love, surprise, joy, sadness and fear are classified through this model and the accuracy of each classifier is calculated.

LSTM, Emotion Recognition

[1] Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, "Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017
[2] Gagandeep Kaur, Kamaldeep Kaur, "Sentiment Detection from Punjabi Text using Support Vector Machine," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.39-46, 2017
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[7] E. Batbaatar, M. Li, and K. H. Ryu, ?Semantic-Emotion Neural Network for Emotion Recognition From Text,? IEEE Access, vol. 7, pp. 111866?111878, 2019.
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[9] K. Mahira, M. Mudasir, H.Nnida, M.Mohsin, ?Emotion analysis:A survey,?, 2017 International Conference on Computer, Communications and Electronics (Comptelix) Manipal University Jaipur, Malaviya National Institute of Technology Jaipur & IRISWORLD, July 01-02, 2017
[10] Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, ?Hierarchical Attention Networks for Document Classification,? Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.
[11] Dr. Ashok Kumar R, Priyanka H S, Ramya B V, ?Classification model to determine the polarity of moviereview using logistic regression,? 2019 International Research Journal of Computer Science (IRJCS) ISSN: 2393-9842, Issue 06, Volume 6,2019
[12] Adarsh S R. (2019). ?Enhancement Of Text Based Emotion Recognition Performances Using Word Clusters.? International Journal of Research - Granthaalayah, 7(1), 238-250. https://doi.org/10.5281/zenodo.2550669.
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V. Preethi, Nimisha Jadav, Komal Shirsat, Mohan Bonde, "Emotion Recognition from Text using LSTM algorithm," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.30-33, 2020.

Diabetic retinopathy is a diabetes complication that affects eyes. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). At first, diabetic retinopathy may cause no symptoms or only mild vision problems however, it can cause blindness. The condition can develop in anyone who has type 1 or type 2 diabetes. It may lead to poor vision and subsequently to complete blindness. This paper presents a Deep Learning approach in detecting Diabetic Retinopathy on Gaussian Filtered Retina Scanned images. We used a Gaussian filtered scan retina image dataset which was downloaded from kaggle.com. This dataset contains five image folders which are Mild folder that contains 370 images of patients with lesser risk to Diabetic Retinopathy (early stage), Moderate Folder contains 999 images of patients having 12%-27% risk of Diabetic Retinopathy, the Severe Folder contains 193 images of patients whose blood vessels have become more blocked, the Proliferate Folder contains 295 images of patients which are on the verge of going on a permanent blindness, the last folder is the No Diabetic Retinopathy folder which contains 1805 images of patients who have no Diabetic Retinopathy. After building and training our convolutional neural network model, the results obtain by the model shows an accuracy of 81.35% at an epoch number of 8. The trained model was saved and tested using flask framework. This model can be deployed to web for detecting and classifying the various categories of diabetic retinopathy.

Gaussian filtered images, Diabetic Retinopathy, Convolutional Neural Network

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P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba, "A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.34-39, 2020.

Every-day is a development of new technology and it creates a revolution in society. The Internet of things is used to combine many devices and communicate with each other to do a specific task. In this paper, we describe a smart system used in supermarkets or shopping malls. Smart trolley with RFID is used to facilitate the users and to know the amount of the purchased items in the trolley. The current system will scan and add the items using microcontrollers. In the existing system, there is a chance of theft activity or if the product does not scan properly and it creates a loss of data. To overcome the drawbacks we proposed a smart trolley with RFID, Arduino Nano, Node MCU esp8266, GSM module which will perform scanning, adding of items, removing of items and theft checking using a load cell and also sends a notification to the mobile user.

Smart trolley, RFID reader, Arduino Nano, Node MCU esp8266

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D. Rajesh, S. Cindhuja, K. DhivyaLakshmi, P. Jaisheela, "Intelligent Trolley based on Internet of Things," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.40-44, 2020.

India is an agro-based economy and proper information about agricultural practices is an essential key to optimal agricultural growth and output. Artificial Intelligence and Machine Learning are driving the IT industry to a new landscape. In order to answer the queries of the farmer, agricultural Chatbot can be designed. The system ?Krushi Mitra? overcomes the problem and provides farmers the better opportunity to obtain the desired information and to scale up with upcoming market trends and technologies in a user friendly manner. Krushi Mitra is actually a chat bot, which is a virtual conversational assistant, through which the users can communicate with the bot as if they are conversing with humans. The focus is on developing the bot in a more intellectual way, that it can even recognize not so well grammatically defined sentences, misspelled words, incomplete phrases, etc. This can help people to converse easily with the bot, since this system uses the Natural Language Processing technique to parse the user queries, identify the keywords, match them with Knowledge Base and respond with the accurate results.

Chatbot, Natural Language Processing (NLP), Agriculture, Prediction Algorithm, Machine Learning

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[10] Vandita Mathad, Greeshma R.R., Harshitha J.V., Deepika S., Snigdha Sen,?Quality Assessment of Crops Through Disease Detection Using Machine Learning?,Vol.8 , Issue.2 , pp.99-102, Feb-2020.
[11] Bhavika Arora , Dheeraj Singh Chaudhary , Mahima Satsangi, Mahima Yadav, Lotika Singh, Prem Sewak Sudhish,?Agribot: A Natural Language Generative Neural Networks Engine for Agricultural Applications?, 2020 International Conference on Contemporary Computing and Applications (IC3A), IEEE 2020.

Utkarsha Kshirsagar, Juee Parte, Mansi Patil, Amit Aylani, "Krushi Mitra: A Review of Agriculture Bots," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.45-50, 2020.

This paper represents survey of various techniques utilized in Credit Card Fraud Detection (CCFD) mechanisms. There are many new and modern techniques depending upon Neural Network (NN) and Artificial Intelligence (AI), Data mining (DM), Artificial Immune System (AIS), Bayesian Network (BN), Fuzzy Logic Based System, Decision Tree (DT), K- nearest neighbor (KNN) algorithm, Support Vector Machine (SVM), Machine Learning (ML), Genetic Programming (GP) etc., which has developed fraudulent transactions to detect various credit card. Various techniques for the FD system have been explained. The powerful FD system, which detects the fraud, but also detects it in a precise manner, is needed in order to stop these frauds. They also need to make our systems learn about or adapt to future new methods of fraud from past frauds. The concept of CC fraud, or its different types, has been introduced in this paper.

Real-time Fraud Detection, Fraud Detection System (FDS), Machine Learning (ML), CCFD Techniques, CCF

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Kapil Dev Tripathi, Vikas Singh Rajput, "Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.51-56, 2020.